Evaluating the next generation of user experience in ...dagda.shef.ac.uk/.../External/SDey_100151932_Souvik_DEY.pdfSouvik Dey Registration No: 100151932 by Date: September 2011 Supervisor:

Evaluating the next generation of user experience in

information retrieval through the visualisation power

of Microsoft PivotViewer

A study submitted in partial fulfilment of the requirements for the degree of Master of Science in

Information Systems Management

at

Souvik Dey

Registration No: 100151932

by

Date: September 2011 Supervisor: Dr. Paul Clough

i

Background: Today, the Web, through its various search engines, has emerged as the

primary source, where people tend to access and gather information from. Searching

for information on the Web has become a key part of our life. However, the interfaces

provided by the major search engines and the techniques used to search content on the

Web, have hardly changed since their inception. There is hardly any support provided

by the pre-existing systems to interact with the required criteria of search, and assist in

formulating and refining queries. After years of development, Microsoft Corporation

has launched a Silverlight control called PivotViewer that allows an interactive

interface, which allows the users to navigate through massive data collections in a way

different from traditional interfaces. This piece of research aims to study this interface

and decide on the possibility of it being a next generation of user interface in

information searching.

Aims: Firstly, this dissertation aims to carry out an investigation of information

retrieval and visualisation systems, along with an exploration of user behaviour in

information searching. Secondly it is aimed to design and implement a Pivot collection

with a large dataset and finally carry out a comparative evaluation and analysis of Pivot

interface with respect to traditional Web interfaces for information retrieval and

visualisation.

Methods: Based on the guidelines set by Microsoft Silverlight Team, a PivotViewer

interface was set up, using 10,000 historic images of St. Andrews University Library. To

evaluate the above system and PivotViewer interface in general, 10 participants from

Information School at the University of Sheffield, was invited to experience this new

interface and carry out tasks in three vivid scenarios. Performance criteria for

evaluations were set and views from the participants were gathered and evaluated.

Results: From the observations and tests carried out, PivotViewer emerged as a clear

winner in terms of speed, as well as the performance criteria set. It provided a rich

multifaceted multimedia visual experience to the users and proved to be more efficient

and acceptable to the users as compared to the traditional search interfaces.

Conclusions: This dissertation illustrates the benefits of incorporating interactive

techniques of information visualisation, through Microsoft PivotViewer, for information

retrieval, keeping in line with the user behaviours in modern day searching. From the

study taken, the Pivot interface can be said to epitomise instances of next-generation

Web search interfaces that has the potential to provide the users to have more active

participation in the information exploration process, from the vastness of the

information space on the Web.

ABSTRACT

ii

This study could not have been accomplished successfully without the assistance of

several people whose contributions I heartily acknowledge. Firstly, I would like to

express my deepest and sincerest appreciation to my supervisor, Dr. Paul Clough,

whose encouragement, support and guidance from the inception of the project, through

to the conclusion, helped me to develop a deeper understanding of the subject. Without

his guidance, this study would not have been possible. Secondly, I would like to

sincerely acknowledge the help of Mark Hall, for his time and expertise, in helping me

out to generate the metadata for the St. Andrews collection. At the same time, I equally

appreciate the help of Dr. Robert Villa, who helped me and guided me on several

occasions, apart from agreeing to carry out my pilot study before the experimentations.

In addition, I would like to express my gratitude to Andy Stones and Paul Fenn for

helping me in resolving technical and system issues, whenever there was a need and I

am also grateful for every support and help received from the Information School at the

University of Sheffield.

It is also a pleasure to thank two of my dear colleagues and friends for their all round

support, in particular, Farhad Sadri and Athira Shridhar. Special thanks are also

extended to Arnab Dutta for his timely advices and guidance. I am also very heartily

thankful to my dearest friends Debanjali Bhattacharjee and Prema Dutta, for their help

in reading and correcting my written work throughout my studies. I would also like to

thank my friends back home Aniruddha Roy, Kaustuv Chatterjee and Subrata Sikdar, for

giving me the moral support and comfort in every step.

Finally, I would like to convey my deepest regards to my family for their endless love

that formed the most important part of my life and for always being there when I

needed them, helping me to face difficulties. Without their undying support and

encouragement, this journey would not have been possible. They gave me an

opportunity to study in one of the best universities in UK, which I shall treasure all my

life.

I would like to dedicate this project to my parents, Papri Dey and Prasanta Dey, in the

hope that I make worthwhile use of my study in UK and live up to their dreams, and

also to my dearest late sister Sohini Hazra, who would be proud of me to hear of all my

achievements till now, if she was here today.

ACKNOWLEDGEMENTS

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Abstract................................................................................................................................................. i Acknowledgement.......................................................................................................................... ii Contents................................................................................................................................................ iii List of figures...................................................................................................................................... v List of tables........................................................................................................................................ vii Chapter 1: Introduction............................................................................................................... 1

1.1 Present Scenario................................................................................................................... 2 1.2 Information and image retrieval systems................................................................. 2 1.3 Efficient Interface Design.................................................................................................. 4 1.4 Structure of Dissertation................................................................................................... 5

Chapter 2: Research Aims........................................................................................................... 6

2.1 Motivation............................................................................................................................... 6 2.2 Research Objectives........................................................................................................... 8 2.3 Research Questions............................................................................................................. 9 2.4 Scope of research................................................................................................................. 9

Chapter 3: Literature Review................................................................................................... 10

3.1 Introduction........................................................................................................................... 10 3.2 Information Retrieval........................................................................................................ 11

3.2.1 Traditional Information Retrieval................................................................. 11 3.2.2 Web-based Information Searching................................................................ 13 3.2.3 Information retrieval of large faceted datasets....................................... 14 3.2.4 Web User Interface............................................................................................... 17

3.3 Information Visualisation................................................................................................ 18 3.3.1 Web-based Information Visualisation.......................................................... 20 3.3.2 Visualisation of faceted datasets..................................................................... 22

3.4 User Behaviour: Web Searching.................................................................................... 23 3.5 Summary................................................................................................................................. 26

Chapter 4: Microsoft PivotViewer.......................................................................................... 27

4.1 Concept of collection.......................................................................................................... 27 4.2 PivotViewer Collection...................................................................................................... 28

4.2.1 Collection XML Schema (CXML)...................................................................... 29 4.2.1.1 Structure of schema................................................................................... 30 4.2.2 Collection Image Content.................................................................................... 31

4.2.2.1 Deep Zoom Image Collections............................................................... 31 4.2.2.2 Deploying Deep Zoom Collections...................................................... 35

4.3 Collection Design................................................................................................................. 37 4.3.1 Kinds of Collections.............................................................................................. 38 4.3.2 Facets and Facet Categories.............................................................................. 39 4.3.3 Organizing Facet Categories in PivotViewer............................................. 40

4.3.4 Facet Naming................................................................................................…………... 42 4.3.5 Facet Values and Formatting................................................................................... 43 4.3.6 Imagery............................................................................................................................. 44

4.4. Summary.................................................................................................................................... 45

CONTENTS

iv

Chapter 5: Research Methodology........................................................................................ 46

5.1 Literature Review............................................................................................................... 46 5.2 Dataset (St Andrews University Library Photographs)..................................... 48 5.3 Overview of metadata...................................................................................................... 49 5.4 Building the Pivot Collection......................................................................................... 50 5.5 Creating Pivot Silverlight Application....................................................................... 54 5.6 Design Checklist.................................................................................................................. 59 5.7 User Experiments............................................................................................................... 60

5.7.1 Ethical Considerations........................................................................................ 61 5.7.2 Selection of participants.................................................................................... 61 5.7.3 Rationale.................................................................................................................. 61 5.7.4 Preliminary Questionnaire............................................................................... 62 5.7.5 Evaluation of user-centred expectations.................................................... 63 5.7.6 Pivot awareness..................................................................................................... 63 5.7.7. A general explanation of using PivotViewer............................................ 64 5.7.8. User-centred interface evaluation................................................................ 65 5.7.9. Interface evaluation parameters................................................................... 67 5.7.10. Post evaluation questionnaire..................................................................... 68

5.8. Pilot Study.............................................................................................................................. 69 5.9. Timetable............................................................................................................................... 70 5.10. Summary............................................................................................................................. 71

Chapter 6: Results and analysis.............................................................................................. 72

6.1 General User Overview..................................................................................................... 72 6.2 Search Behaviour Patterns.............................................................................................. 74 6.3 Evaluation of user-centred expectations.................................................................. 80 6.4 Microsoft PivotViewer awareness................................................................................ 81 6.5 User-centred interface evaluations............................................................................. 82

6.5.1 Scenario: A................................................................................................................ 82 6.5.2 Scenario: B................................................................................................................ 84 6.5.2 Scenario: C................................................................................................................ 87

6.6 Summary of interface evaluations............................................................................... 89 6.7. Post Experimentation Results: 1................................................................................. 91

6.7.1 Evaluation of Tasks.............................................................................................. 91 6.7.2 Results on Pivot Interface.................................................................................. 91 6.7.3 Technique and Quality of Results Retrieved by PivotViewer............ 92

6.8. Post Experimentation Results - 2................................................................................ 94 6.9. Summary................................................................................................................................ 97

Chapter 7: Discussions................................................................................................................. 98 7.1 PivotViewer as an Information Retrieval System.................................................. 99 7.2 PivotViewer as an Information Visualisation System.......................................... 101 7.3. Summary................................................................................................................................. 102

Chapter 8: Conclusion................................................................................................................... 103

8.1 Research Summary............................................................................................................. 103 8.2 List of Objectives.................................................................................................................. 104 8.3 Results....................................................................................................................................... 106 8.4 Limitations.............................................................................................................................. 106 8.5 Future Research.....................................................................................……………….…… 107 8.6 Summary…………………………………................................................................................. 109

Appendices.......................................................................................................................................... 111 References........................................................................................................................................... 126

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Fig 1: Images returned by Google Image when queried for “Endangered

Species” [July 20, 2011]..................................................................................................... 4 Fig 2: Scope of this dissertation.................................................................................................... 9 Fig 3.1: Yahoo Directory................................................................................................................. 15

Fig 3.2: The Phlat interface with a query of a single keyword and two filters………………………………...................................................................................................... 16

Fig 3.3: FacetMap Interface........................................................................................................ 16

Fig 3.4: Primary colours as described in Opponent Process Theory……...... 19 Fig 3.5: FacetLens interface........................................................................................................ 20 Fig 3.6: FacetMap interface......................................................................................................... 20

Fig 3.7: Grokker - Information visualisation......................................................................... 23 Fig 4.1: Example of a Pivot interface.......................................................................................... 27 Fig 4.2: Pivot Architecture.............................................................................................................. 28 Fig 4.3: Simple example of a CXML file...................................................................................... 29

Fig 4.4: Simple Pivot Interface based on the previous sample CXML code. 30 Fig 4.5: Structure of schema.......................................................................................................... 31

Fig 4.6: DZI – DZC based on Morton Layout.................................................................... 32 Fig 4.7: Iterative process in designing..................................................................................... 37 Fig 4.8: Facets and Facet Categories.......................................................................................... 39 Fig 4.9: Information Panel- I......................................................................................................... 40 Fig 4.10: Information Panel: II..................................................................................................... 41 Fig 4.11: Guidelines for facet values.......................................................................................... 43 Fig 4.12: Example for enhanced collection imagery........................................................... 44 Fig 5.1: Example of metadata associated with images in St. Andrews University

Library historic photographic collection............................................................... 49 Fig 5.2: First step to start building the collection................................................................. 51 Fig 5.3: Columns representing facet categories for St. Andrews Collection ........... 52 Fig 5.4: Image showing Pivot ‘Publish Button’...................................................................... 54 Fig 5.5: Creating Pivot Silverlight Application...................................................................... 54 Fig 5.6: Mainpage.xaml..................................................................................................................... 55 Fig 5.7: MainPage.xaml.cs................................................................................................................ 56 Fig 5.8: HTML coding to bind CXML............................................................................................ 57 Fig 5.9: Pivot Collection of St. Andrews University photographs.................................. 58 Fig 5.10: Facet categories in Pivot collection of St. Andrews University

photographs..................................................................................................................... 58 Fig 5.11: Various features of Pivot Interface ........................................................................ 64 Fig 5.12: St. Andrews Interface and Pivot interface with exactly same datasets.... 65 Fig 5.13: Endangered species Pivot interface and Google Images.............................. 66 Fig 5.14: IMDb and Netflix PivotViewer .................................................................................. 66 Fig 6.1: Participant age group....................................................................................................... 72 Fig 6.2: Professional background of participants................................................................ 72 Fig 6.3: Educational background of participants................................................................. 73 Fig 6.4: User PC familiarity............................................................................................................. 73 Fig 6.5: User photo search experience....................................................................................... 73 Fig 6.6: User Internet usage........................................................................................................... 74 Fig 6.7: Confidence levels in Web searching........................................................................... 74 Fig 6.8: Online search habits ......................................................................................................... 75

LIST OF FIGURES

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Fig 6.9: User satisfaction levels on text-based searching ................................................. 76 Fig 6.10: User satisfaction levels in image searching.......................................................... 76 Fig 6.11: Overall traditional search interface satisfaction levels.................................. 76 Fig 6.12: Number of pages viewed by users, before they usually stop

searching for information........................................................................................... 77 Fig 6.13: Graph indicating the favourable image search techniques (for each

questions a to e) and their corresponding frequency/percentage of support................................................................................................................................. 78

Fig 6.14: Graph indicating the array of experiences with image searching techniques (for each questions a to d) and their corresponding frequency/percentage of support............................................................................. 78

Fig 6.15: User-centred expectations........................................................................................... 80 Fig 6.16: PivotViewer awareness................................................................................................. 81 Fig 6.17: Familiarity of using Microsoft PivotViewer......................................................... 81 Fig 6.18: Graphs comparing the time taken and number of clicks for completing

scenario-A- Task1 and 2................................................................................................. 82 Fig 6.19: Interface evaluation summary- scenario-A.......................................................... 84 Fig 6.20: Graphs comparing the time taken and number of clicks for completing

scenario-B- Task1 and 2................................................................................................. 85 Fig 6.21: Interface evaluation summary- scenario-B.......................................................... 86 Fig 6.22: Graphs comparing the time taken and of number clicks for completing

scenario-C- Task1 and 2................................................................................................. 87 Fig 6.23: Mean rating summary for scenario-C..................................................................... 89 Fig 6.24: Summary of time taken and number of clicks for all tasks............................ 90 Fig 6.25: Overall interface evaluation summary................................................................... 90 Fig 6.26: Analysis of tasks undertaken...................................................................................... 91 Fig 6.27: Analysis of Pivot interface........................................................................................... 92 Fig 6.28: Analysis of technique/quality of results retrieved by PivotViewer.......... 93

vii

Table 3.1: Basic section to encompass a system.............................................................................. 11 Table 3.2: Collection of studies on Information Visualisation................................................... 19 Table 3.3: Categorised visualisation dimensions and related studies................................... 21 Table 3.4: User behavioural pattern studies..................................................................................... 23 Table 4.1: Organisation of DZI and DZC............................................................................................... 33 Table 4.2: Summary of procedures to build Pivot Collection.................................................... 33 Table 4.3: Comparative chart between JPEG and PNG formats................................................ 34 Table 4.4: Kinds of Collections…………………………………………….................................................. 38 Table 5.1: Evaluation methodologies carried out in previous studies................................... 47 Table 5.2: System requirements for executing and building PivotViewer applications 50 Table 5.3: Settings of St Andrews collection..................................................................................... 53 Table 5.4: Miscellaneous Property Adjustments............................................................................. 53 Table 5.5: Design Checklist....................................................................................................................... 60 Table 5.6: Parameters for interface evaluation................................................................................ 67 Table 5.7: Post evaluation questionnaire categories Part- 1...................................................... 68 Table 5.8: Approximate time needed for total experiment......................................................... 70 Table 6.1: Participant age groups........................................................................................................... 73 Table 6.2: User professions....................................................................................................................... 73 Table 6.3: Educational background of participants....................................................................... 73 Table 6.4: Familiarity with PC................................................................................................................. 73 Table 6.5: User photo search experience........................................................................................... 73 Table 6.6: User Internet usage................................................................................................................. 74 Table 6.7: User confidence levels when searching the web....................................................... 74 Table 6.8: Online search habits............................................................................................................... 75 Table 6.9: Responses to favourable search topics ......................................................................... 75 Table 6.10: User satisfaction levels for text-based searching ................................................... 76 Table 6.11: User satisfaction levels for image searching............................................................. 76 Table 6.12: Overall traditional search interface satisfaction levels........................................ 76 Table 6.13: Number of pages viewed by users, before they usually stop searching for

information.............................................................................................................................. 77 Table 6.14: Table indicating the favourable image search techniques and their corresponding frequency/percentage of support by users................................77 Table 6.15: Table indicating the array of experiences with image searching

techniques and their corresponding frequency/percentage of support by users.................................................................................................................................... 78 Table 6.16: Table indicating the users’ expectation levels on certain features in user interfaces for information visualisation and retrieval.............................. 80 Table 6.17: Familiarity with using Microsoft PivotViewer........................................................ 81 Table 6.18: Time taken for carrying out each of the tasks and the number of clicks- Scenario-A Interfaces.......................................................................................... 82 Table 6.19: Performance results based on five performance criteria for St. Andrews

interface.................................................................................................................................. 83 Table 6.20: Performance results based on five performance criteria for PivotViewer

interface................................................................................................................................... 83 Table 6.21: Mean rating summary for scenario-A ......................................................................... 84 Table 6.22: Time taken to carry out each of the tasks and the number of clicks-

Scenario B................................................................................................................................. 84 Table 6.23: Results based on five performance criteria for Google Images....................... 86 Table 6.24: Results based on five performance criteria for PivotViewer........................... 86 Table 6.25: Mean rating summary for scenario-B ......................................................................... 86 Table 6.26: Time taken for carrying out each of the tasks and the number of clicks-

Scenario-C ............................................................................................................................... 87 Table 6.27: Results based on five performance criteria for IMDB ........................................ 88

LIST OF TABLES

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Table 6.28: Results based on five performance criteria for Netflix Pivot.............……….. 88 Table 6.29: Interface evaluation summary- scenario-C.............................................................. 89 Table 6.30: Summary of individual task results between PivotViewer and other

(traditional) interfaces....................................................................................................... 89 Table 6.31: Overall performance summary ..................................................................................... 90 Table 6.32: Results of tasks undertaken............................................................................................. 91 Table 6.33: Results of Pivot interface ................................................................................................. 92 Table 6.34: Technique and Quality of results retrieved.............................................................. 92 Table 6.35: User views on PivotViewer advantages..................................................................... 94 Table 6.36: User views on PivotViewer’s disadvantages............................................................ 95 Table 6.37: Extra functionalities and recommendations for interfaces............................... 96 Table 6.38: Additional comments......................................................................................................... 96

Word Count

Sections included: 1 (Introduction) to 8 (Conclusion)

Pages included: 1 to 110 (excluding tables and figure names)

Package used: Microsoft Word 2007

Word Count: 25,986

INF 6000 – Dissertation – Microsoft PivotViewer 2011

Year – 2011 Page 1 [email protected] - 100151932

Chapter 1

With the exponential increase in the plethora of information generated in the world

today, the design of visually interactive representation of information is undergoing a

substantial paradigm of transition through technological advancement, with the

persistent maturation of our information age. A rapid transformation in the cyber age

with the centralisation of data and ubiquity of information digitization, coupled with the

technological advancements in networking architectures, has resulted into a radical

growth in the bandwidth of information consumption and use. The rate at which this

information is growing, it becomes increasingly significant to find an efficient solution to

manage and explore the required information from this abundance for analysis. Often, it

turns out to be quite time-consuming when a particular information or data needs to be

manually fetched from this abundance. Besides, there is also a need for proper

scalability and accessibility in the method of such a representation because, although the

required data may be fetched, it is more difficult to establish an inter-relationship with

the other data collections in other places or across the website, which further

complicates the process. However, with the advancements in Computer Graphics

technology, we are now stepping into an era where large scale analysis of quantitative as

well as qualitative data is now at reality. But then, even with such a steadfast progress,

there is a need for addressing the satisfaction level in the paradigm of human cognitive

process, when it comes to using the traditional – text based and often page based

information or image retrieval systems.

INTRODUCTION



1.1 Present Scenario

Information retrieval, storage technologies, manipulation and analysis of data in the

form of pictures, texts and numbers have been successfully dealt with over the years,

with the help of hybrid technologies and efficient computer computing systems. Today,

we have several useful applications in the form of database technologies, spreadsheets

and word-processors that go miles in simplifying several of our projects, businesses and

tasks that were initially complicated, strenuous, and quite time-consuming. Massive

developments in computer science and technology, precisely in improving the speed of

processing and allowing machines to manipulate huge amounts of information, that

include not just texts and images, but also applications that are capable of handling data

in the form of user generated video and audio resources are in abundance. However,

Nguyen and Huang (2004) point out,

These problems with the current user interfaces have led many designers of

information systems to realize the need for the development of new generation

user interfaces that can reduce the human cognitive cost for their next generation

information systems.

With every passing second, there is an increasingly high amount of usage of such

applications that generate additional magnitudes of multimedia and as such, a huge

amount of data and information are being added to the global information network. Such

an exponential growth also inevitably allows these collections to be globally available on

the World Wide Web and get heaped amongst the abundance of their ubiquity.

1.2 Information and image retrieval systems

The principle work of a proper image retrieval system is to help users retrieve a certain

category of image or images effectively in the least amount of time from the repository.

Years of research and study in the area of information retrieval have significantly

improved the technology behind such systems by introducing automated techniques that

indexed documents based on the words which could be easily retrieved by users through

queries. However, image retrieval is more challenging, given the fact that indexing

images is complicated since unlike textual information, they are devoid of units that have

a proper meaning which would enable a quick extract. As such, images are often

associated with some meta-data or a set of descriptive text, comprising of categories,

”

“



titles and keywords which enable images to be extracted and filtered using traditional

information retrieval methodologies. For example, if a tourist wants to locate the picture

of a wonderful sea beach in a particular country, in order to make a decision for a

vacation, he can simply enter “sea beach” as a contextual query or search the related

category, assuming the image collection has the suitable metadata associated with it.

In text based information retrieval systems, the result extracted is generally a file or a

group of files which have an associated filename and a preview of its contents or

highlighted text that mark the query. Users can overview these results and make a

decision on its relevance to their requirements, however, with images, only a miniature

version called thumbnail is visible, seeing which the user can narrow down and decide

on his relevant requirements by judgements. However, there may be many other images

in abundance on the Web that might be hidden beneath the huge repository of pictures

that he may not come across initially.

Short Scenario

If we consider pictures of endangered species, for example: there may be various species,

with different originating locations and which may have different class and family. Taking

into consideration a popular search engine, such as Google Images (or Flickr, Picasa etc.), if

we make a query on “Endangered Species”, it will return a collection of thousands of

images with related web pages that may often be irrelevant or may require us longer time

to realize exactly what we want (fig-1). Besides, it may not be exactly what we were

looking for and our actual requirement may be hidden beneath the millions of results that

were processed. The large numbers of thumbnails that are retrieved are however limited

to per page views and the user will need to browse further or refine the search query in

order to narrow down to the actual requirements which, if complicated, may take ages.

Besides, documenting annotations are very laborious and the results are most often quite

subjective. As such, research and development studies in image retrieval are mostly

focussed on generating objective methods for spontaneously indexing images.



1.3 Efficient Interface Design

Interface design for information retrieval and human computer interaction needs to be

future-proof by taking technology to new heights and obscuring the perimeters between

metadata and content, and the boundaries that segregate explicit from the implicit and

public from private. From a collection of navigable pages to a juxtaposition of vibrant

knowledge pattern, the perspective on Web and technology is constantly repositioning

itself into our society by feeding a vivid accumulation of information sources in a

bionomic array of services. The core of this process lies in the exponential increase of

social web-applications, coupled with the structural modifications lead by the

pervasiveness of web-network applications and advanced technological devices, which

has initiated a redefinition of our understanding and knowledge of information retrieval,

architecture, visualisation, communication and storage.

In order to create an effective user experience in this area, a profound perception of

upcoming format of contents, along with the knowledge and perception of technological

changes and understanding of the character of metadata is essential. If carefully

observed, there is an increasing trend in the rise of broadly structured, subjective and

transient data available which emanates into creating a hybrid inter-subjective, multi-

faceted array of contents associated with people around the globe and metadata. As such,

novel advancements are required to manage rising paradigms such as visualisation of

information snippets and semantic collaborative tagging.

Fig 1 – Images returned by Google Image when queried for “Endangered Species” [Accessed: July 20, 2011]



1.4 Structure of Dissertation

Chapter 2 provides the research motivations for carrying out this project, along with the

research aims and related objectives. Based on these, a few research questions are asked

and the scope of the project has been laid down.

Chapter 3 furnishes an exploration on the background to this research, contextualising it

in the area of information retrieval, information visualisation and user behaviour in

information searching. Relevant works by authors have been discussed in these areas, in

the chapter, to formulate the ideas percolating on these areas and enable an insight to

the project taken.

Chapter 4 introduces the PivotViewer interface and discusses the concept of

PivotViewer collections and architecture. Schema and image content formats are briefly

discussed, along with the procedures for collection design, with explanations based on

facets and facet categories. Summaries on facet naming, organising, formatting and

quality of imagery have been discussed

Chapter 5 describes the research methodology undertaken to carry out the dissertation,

by discussing the relevant literature. It also focuses on the practical implementation

details in building the PivotViewer application in Silverlight, with focus on the dataset

adopted and the steps undertaken. Finally, the methods of user experiments are

discussed in details with the views on ethical considerations undertaken and the

rationale.

Chapter 6 presents the findings of the experiments undertaken in to four categories:

general user overview and search behaviour patterns, leading to the analysis of the user-

centred expectations, evaluations between PivotViewer and traditional interface and

post experimentation evaluations.

Chapter 7 discusses the results obtained from the user experiments and relates them to

the literature discussed in Chapter 3, with the view to answering the research questions

posed in Chapter 2.

Chapter 8 summarises the main findings of this research, stating the aims and objectives

achieved in the research, followed by the limitations encountered and the possibilities

for further work.



Chapter 2

2.1 Motivation

Ever since the digital space in the Web flagged off with the dominance of the hypertext, it

has transformed into an age of advanced improvements and break-through technologies,

especially in visualisation and graphics with the sophistication of human interactivities.

It simultaneously weaved diverse resources that comprised of semantic, spatial, dynamic

and social relationships with rich links. As such, technological advancements have

rapidly increased the information and data resource domains on the Web and it is

dynamically expanding with every passing second. While, it is evident that there has

been a significant contribution and development in Web based search engines that are

capable of indexing a magnitude of documents and information, and returning the results

in near fraction of seconds (Brin and Page, 1988; Ghemawat et al., 2003), the user

experiences in information seeking behaviour and the level of interactivities have not

seen much progress and there has been no drastic change. The process of crafting

queries and the methods to deliver the queried results have fundamentally remained

unchanged. According to Broder et al. (2010), pages and applications with a clean query

bar has always been the traditional form interfaces where users feed textual queries, and

are presented with a format that is traditionally an array of list-based textual hyperlinks

or “10 blue links”, each essentially with a title, followed by short snippets comprising of

the queried texts and an associated URL to allow the users to be redirected to the

particular document or website so that they can scavenge their required information.

Throughout the years, these generic interfaces have been canonically used for

exploratory searching either in simple or complex chained queries, resulting in the

generated catalogued result sets. Though such an approach may direct the users to

retrieve relevant information, it could however still be difficult to accumulate an overall

inter-relation of the whole set of collection of resources that are available on that

particular topic or query from the information space and gather a sense of orientation.

RESEARCH AIMS



Research and analysis on Web search behaviour of users have demonstrated that a

significant number of Web based search queries comprise of hardly one to three terms

(Jansen and Pooch, 2001; Spink et. al. 2001). These indicate that users seeking

information from search engines frequently face complexities in formulating or

constructing queries that find their most precise information requirements. Arguably,

search engines today provide hardly any support for users in reconstructing or

formulating their queries and it is often the onus of the users to adjudicate on the words

to use during the subsequent manual querying. Consequently, Web users rarely persist

in crafting further alterations to their queries (Silverstein et al., 1993; Spink et. al. 2001).

The traditional static forms of display of Web search results might have been a driving

factor to such user behaviour. The users are hardly given the choice to explore or play

around with the fetched results and as such, they have to resort to considering each

result individually and also in a given sequence.

Attributing to this present scenario, the theme of this piece of research work is to

demonstrate these underlying issues and instituting and evaluating a novel paradigm of

visually interactive next-generation of information retrieval interface, as provided by a

Microsoft product called PivotViewer. For the purpose of the research, an interface is

developed using the above technology on a set of image collection from a pre-existing

search database. At the same time, a couple of other ready-made pivot models or

prototypes designed by organisations have been taken into consideration for the

purpose of evaluation, with search based interfaces on similar collections present today.

These would empower the users to participate in a rather active role in working with the

information retrieval process provided by these systems, which will then be monitored

and evaluated through a set of feedback based on their experiences. The capability of the

users to explore their search results and at the same time, interacting and co-relating the

retrieved results illustrates and signifies a leap towards the vision Yao had for Web

information retrieval process systems (Yao, 2002).

This dissertation recognizes the importance of fundamental human decision making

while formulating queries from the information space, as they attempt to identify and

retrieve useful and relevant information according to their needs. Through the

development of such a next-generation interface using Microsoft PivotViewer, there will

be a substantial impact and knowledge on how users retrieve a needle of information

from an ever growing hay-stack of information space in the future.

“The public has a low tolerance of going in depth through what is retrieved”

- Spink et al. (2001),



2.2 Research Objectives

The central theme of the research is to carry out a qualitative analysis by gathering a

significant understanding of information visualisation and establish a relation by

investigating how the interactive visualisation technique of Microsoft Pivot Viewer can

sustain information explorers to seek available information, through the massive amount

of data available on the Web.

As such, with my research I aim to fulfil the following aims and objectives:

Aim -1

To carry out a study-oriented investigation of information retrieval and visualisation

systems, along with the exploration of associated user behaviour, in information searching.

Objective 1: Investigate related literature in the areas of:

o Information Retrieval

o Information Visualisation

o User behaviour in information searching

Aim -2

Design and implement a Pivot collection with a large dataset.

Objective 1:

To study the techniques and features, to design and build pivot collections.

Objective 2:

To transform the historic photo collection available in St. Andrews University Library,

into a prototype Pivot collection.

Aim -3

To carry out a comparative evaluation and analysis of Pivot interface with respect to

traditional Web interfaces for information retrieval and visualisation.

Objective 1: To perform a comparative performance evaluation between the Pivot

interface developed on St. Andrews University photographic collection and the original

St. Andrews University Library interface and explore the results.

Objective 2: To perform a comparative analysis and performance evaluation, on

image retrieval on a particular subject, between an open-ended interface (Google

Images), with that of a Pivot interface on the particular subject.

Objective 3: To perform a comparative analysis and evaluation of two interfaces

dedicated to the same domain of information retrieval service - IMDb and Netflix

PivotViewer interface.



2.3 Research Questions

The fundamental hypothesis related to this research is that information retrieval through

visualisation by Microsoft PivotViewer is a novel mechanism of information seeing that

can substitute established query based or web-based interaction methodologies, in

terms of a higher multi-level way of navigating magnanimous data collections. To test

this hypothesis, I shall pose the following research questions:

Q1: How can information visualisation through Microsoft PivotViewer help to explore

information?

Q2: How can interactive exploration system through Microsoft PivotViewer be

architected and developed according to resources available on St. Andrews University

photo collection?

Q3: What are the ways of improvements and effects of information retrieval through

visualisations using PivotViewer on resource seeking?

2.4 Scope of research

There are three major research communities that could be adequately associated with

my research interests that encompass the domain of my research questions:

Q1: Web Research

Q2: Information Visualisation

Q3: Information Retrieval

This piece of research shall however focus at the cross-section of Information

Visualisation and Information Seeking that is Visualisation for Information Retrieval

(Fig-1). However it is worth mentioning that the research will also touch the

fundamental aspects of Human Computer Interaction.

Information

Visualisation

Information

Retrieval

Web Research

Visualisation for

Information

Retrieval

Visualisation for Web-

based Search and

Exploration

Information

Visualisati

on on the

web

Web based Information

Retrieval

Fig- 2: Scope of this

dissertation



Chapter 3

3.1 Introduction

Searching for information is an inherent part of our lives and the rapid progress of

technology is playing a significant role in the area of information visualisation and

retrieval to help it in its evolution. Earlier, studies and articles related to the area of

visualisation and information retrieval was mostly authored and limited within the

domain of computer scientists (Chen, 2003). Today, there are several books that are

focussing on the algorithms and methods of information retrieval systems (Hearst, 2009;

Andrews, 2006). Over the years, due to technological developments, there has been an

exponential growth of interest and activities in the area, which has lead to the

publication of several articles in recent years that depicts novel visualisation systems

(Andrews, 2006; Chau, 2011, Chen, 2003; Dix et al, 2010, Hearst, 2009). While, some

present new algorithmic approaches (Nguyen et al, 2005), and others have carried out

detailed studies with human interactions for information retrieval (Whittaker et al,

2009), research on user interfaces are still being considered rare (Hearst, 2009). There is

a rising number in the taxonomies of information visualisation techniques (Chen, 2003)

and research in this domain is now disseminated across several platforms.

This chapter aims to discuss the three important areas of information and computer

science that this dissertation relates:

Information Retrieval

Information Visualisation

User behaviour in Web searching

For the purpose of this literature review, a ‘T-Shaped’ path is chosen, whereby mostly

studies related to a broader overview of certain areas are discussed (symbolising the

head of ‘T’), and in other cases, an in depth and ‘longer’ analysis of an area is taken into

consideration (symbolising a bottom half of ‘T’). Careful selection of literature is

gathered for the purpose of both, the practical engineering of an interface as well as to

contribute to the academic world an essential and synthesised set of ideas.

LITERATURE REVIEW



3.2 Information Retrieval

Information Retrieval is defined as the process that deals with the representation,

storage, organisation of, and access to information items (Baeza-Yates and Ribeiro-Neto,

1999). This is the science, that essentially involves an automated process, which is

carried out by custom a developed system, that index digital document collections

automatically (Hoeber, 2007; Rodden, 2002).

3.2.1 Traditional Information Retrieval

Traditionally, an information retrieval system involves extracting words individually

from a collection of digital documents and associating them uniquely with a pointer that

links them to the set relevant documents, according to the information fed by the user,

while at the same time, minimising as many non-relevant documents as possible

(Rijsbergen, 1979) . Users can query a text to search for a required topic or fetch a

document for retrieving particular information. The system in concern processes the

query and retrieves the documents that contain the particular text in query. This may be

presented in a format that is sorted either by an estimated relevance or ordered

according to the frequency of the term in the document(s). Though, the significance of

information retrieval has been broadly on text and documents (Voorhees, 2004), similar

technique have also been applied successfully on retrieving images, audio and videos

(Baeza-Yates and Ribeiro-Neto, 1999).

Traditionally, there are two basic sections that encompass such a system:

Input User query

Digital document collection

Output Set of relevant documents matching the query

It is however said to be a good challenge to optimise the data structures to accommodate

documents as well as the queries that saves considerable amount of memory, but at the

same time, facilitates an efficient and precise coordination of matching queries to the

“But do you know that, although I have kept the diary [on a phonograph] for months

past, it never once struck me how I was going to find any particular part of it in case I

wanted to look it up?”

Dr Seward, Bram Stoker's Dracula, 1897

Table 3.1: Information Retrieval System



digital documents. Matching may be carried out in either of the following ways (Baeza-

Yates and Ribeiro-Neto, 1999, Rijsbergen, 1979):

Boolean Matching

Vector-based Matching

Fuzzy Matching

Probabilistic Matching

There is a considerable amount of benefit in indexing and saving the internal

representation of this collection, for efficient querying with time. Appropriate precision

metric and recall metrics are used to order the fetched results in accordance to the

information need and relevance of documents (Hoeber, 2007). Studies carried out

earlier, acknowledged feedback as the measure of improvising effective retrieval

(Rijsbergen, 1979), however gradually it transcended to user’s crafting of better queries,

and slowly further extending to interactive and automated interfaces, such as query

expansion (Efthimiadis, 1996).

Contrary to traditional systems involving queries and ways to improve precision (Baeza-

Yates and Ribeiro-Neto, 1999), several researches have slowly been carried out in this

area where focus was given towards researcher’s motivations, such as Bates (1989), who

characterised information sources and search tools as ‘evolving berrypicking’, where

digital information search comprised of multiple successive queries in the information

space. Quite similarly, Kuhlthatu (1991) exclaims in his paper that information seeking is

not just about the queries, but rather a dynamic process, that involves the user’s

‘thoughts, feelings and actions’. In another theory suggested by Pirolli et al. (2001), called

information foraging theory, it is said that while searching, “humans are drawn

towards groups of relevant items in an analogous way to animals attempting to locate

dense patches of food in the wild”. Besides, researchers have also carried out studies to

automate digital document collection into clusters or groups, according to some

common words that correlate them (Rodden, 2002), which was thought to be more

efficient than matching documents individually using queries (Maarek et al, 1991;

Jardine and Rijsbergen, 1971); however this has not been proved (Willet, 1988). The

cluster hypothesis articulates that:

“Closely associated documents tend to be relevant to the same requests”

(Rijsbergen, 1979), or in other words,

“Relevant documents tend to be more similar to each other than non-relevant

documents” (Hearst and Pedersen, 1996).



As user requirements related to finding an information may often also be vague (Rodden,

2002), Pirolli and his associates demonstrated that clustering presented the users with

an overview of related retrieved information(Pirolli et al, 1996), which was further

confirmed in theory by Hearst and Pedersen (1996) using Scatter/Gather system

(Cutting el al, 1992). Kural et. al (2001) states that the effectiveness of query clustering is

however dependant on the understanding by the users and the compactness in the

representation of the clusters. Besides, according to information foraging theory, the

measure of information scent, defined as - “the strength of local cues ... in providing an

indication of the utility of relevance of navigational path leading to some digital

information source” (Pirolli et al. 2001), is also determined by the information clustering

quality. However, according to Furnas (1997), traditional querying is always a good

point of starting as it is often hard to ascertain a quality information scent.

3.2.2 Web-based Information Searching

Retrieving information from the universal hypertext space has amalgamated deeply with

our society today (Lawrence and Giles, 1999) with a reported statistic of around 88%

users using the Web search engines to carry out certain tasks (Nielsen, 2004).

Additionally supported by previous studies, a reported 85% new Web pages are

reported to be found using search engines (Graphics, Visualization, & Usability Center,

1998). According to Nielsen, Web search engines have slowly transformed over the years

into ‘answering engines’, with people generally harvesting specific information(Nielsen,

2004), without deeper exploring into individual web sites, guided by their natural and

instinctive ‘low tolerance’ level (Spink et al., 2001). This is evidently due to the

magnanimity of the ever growing collection being added every second and ‘potential

generality of individual documents’ (Hoeber, 2007).

Web-based information retrieval is said to have always been considered hand in hand

between searching and browsing (Bates, 1989). This kind of information gathering may

often involve crafting several queries which might tend to be complicated with

expanding information sources, considering innumerable individual and unknown items

that are on the rise. Although there have been considerable improvements in the

performance and efficiencies of search engines over the past few years, instances of

insignificant and irrelevant documents, surprisingly often in the top 10 to 5 list, are still

on the rise (Chau, 2011). We might consider the Web as a single distributed repository of

information collection, however traditional information retrieval approaches are said to

be practically inefficient to handle it (Yang, 2005). According to Yang, retrieving



information from the Web “must deal with mostly short and unfocused queries posed

against a massive collection of heterogeneous, hyper-linked documents that change

dynamically” (Yang, 2005), which is a well defined challenge compared to traditional

retrieval systems.

Web search-engines traditionally assist the users with a brief summary of the pages, in

the presented list of retrieved results, which allow the users to perceive the contents, so

that they could click to browse it and obtain the potential information that they seek.

Apart from highlighted keywords (McDonald and Chen 2006), further data inform of

page popularity, relevant score, frequency of keywords etc. provide additional assistance

to the users to search their required information (Brin and Page 1998). One good

example in this respect is Google search engine’s PageRank algorithm (Brin and Page,

1998). These attributes, comprising of textual or numerical values, help organise the list

of retrieved results and ensure directing the users’ perception towards the quality and

relevance of the material, so that they can make a better judgement in utilising their time

and effort in exploring them further (Chau, 2011). Once again, as demonstrated by

Spink, Wolfram, Jansen, and Saracevic that users generally have a “low tolerance of going

in depth through what is retrieved” (Spink et al. 2001), and hence, this may sometimes

lead to be an exhaustive and often futile if they rely only on a subset of such attributes in

making a judgement and go through links and documents irrelevant to their queries

(Chaur, 2011).

Given the scope of this dissertation, since this piece of research is not directed deeper in

this field, for further interest reader may follow further papers, journals and books in

this area (Baeza-Yates and Ribeiro-Neto, 1999; Kobayashi and Takeda, 2000; Kosala and

Blockeel, 2000; Rasmussen, 2003; Yang, 2005).

3.2.3 Information retrieval of large faceted datasets

In order to retrieve information from large datasets, Smith et al (2006) has suggested the

use of interactive exploration through ‘categorisation’ or ‘browseable hierarchy’. Such

a technique is said to leverage the power of log (n) to quickly retrieve the required item

from a large aggregated set of items. One such example is the Yahoo search portal

(dir.yahoo.com) shown in Fig 3.1.



However, one drawback of such a system is that it is difficult to come to a conclusion

regarding the best set of categorisation, as the facets may be evolving. Several other

studies have been carried out (Zeng et al., 2004; Sanderson and Croft, 1999; Cutting et

al., 1992; Pirolli et al, 1996) to handle large datasets using automatic clustering, that can

be said to have some relation with categorisation (Smith et al, 2006). According to Smith

and his colleagues, such techniques are also apparently target-oriented instead of

matching textual similarities. But, some still provide additional facilities such as dual-

mode interfaces, by clustering the search information only when initial keyword

retrieval is completed (Sheng et al., 2004).

To refine categorisations in the representation of data collections, the use of ‘faceted

metadata’ is seeing an increasing importance recently (Smith et al., 2006). ‘Facets’ are a

set of orthogonal categories that represent the attributes or metadata of the data

collection. For example, a historic collection of photographs may have thousands of

images with metadata, such as ‘location’, ‘year’, ‘originator’ etc. These can be categorised

into facets and represented visually. In a system by Yee et al. (2003) called Flamenco

system, an efficient and interactive user experience has also been demonstrated through

faceted metadata integration into dynamic-query interface. Besides, another system

called Phlat (Fig 3.2) (Curtell et al, 2006), as emphasised information retrieval using

faceted interface through user- tag integration.

Fig 3.1: Yahoo Directory



Several organisations have also capitalised such a technology and have commercialised

them:

i411 (now IntelligenX - www.intelligenx.com)

Endeca (www.endeca.com)

Inxight (now owned by SAP)

FacetMap (Smith et al., 2006) also demonstrated several features in the use of facets

including query previewing and top-level organisational interface system (Fig. 3.3). But

unlike most other interfaces, it also has dynamically adjustable screen space allocation

based on the retrieved items.

Fig 3.2: The Phlat interface with a query of a single keyword and two filters

Fig 3.3: FacetMap Interface



3.2.4 Web User Interface

A certain area worth mentioning, however, which apparently has a comparatively lacking

attention in this area of information retrieval on the Web, is the research on interface.

(Hoeber, 2007). Although the user interface is the main access point of the system, there

is a dearth of recognition on the significance of the functionality and features of the user

interface, in terms of providing the users a proper means to carry out their queries or

filter their retrieved results. Apparently, a simple text box where users can feed their

queries followed by a section below that displays list-based search results have been

mostly prevalent amongst all major engines such as Google, Yahoo etc. There are not

many studies, however, if such a feature is actually adequate enough to assist the users

(Hoeber, 2007).

According to Marchionini (1992),

Much research and development is required to produce interfaces that allow end

users to be productive amid such complexity and that protect them from

information overload.

His remarks also render further impetus in this study on interactive information

retrieval and information visualisation, which shall be carried out through Microsoft

PivotViewer particularly in this study.

” “



3.3 Information Visualisation

Information visualisation is process of representing either abstract data, static or

temporal information and related concepts in the form of a graphical and visually

attractive form (Ware, 2004; Spence 2001). The prime aspect of information

visualisation is, to accelerate human cognitive understanding, by capitalising on their

visual information processing capabilities (Spence, 2001; Shneiderman, 1996). In other

words, information visualisation provides a bridge between the computer interface

systems and the human vision (Zhu and Chen, 2005) and catalyses them with tools that

amplify their cognitive intelligence (Chen 2004, Spence, 2001; Card et al, 1999;

Shneiderman, 1996). Information visualisation facilitates in representing a large or

complex set of abstract data in a pellucid and consistent manner, by giving the user the

possibility to explore and compare the inter relations between the various data visually

(Tuft, 1990).

Dimensions and attributes that may be associated with the data such as – spatial

coordinates, shape, colour etc. are required to be carefully considered, while designing

the interface (Hoeber, 2007) so that they can be perceived easily by the user. Ware has

suggested some more excellent information on this topic in his book, especially

regarding spatial location and colour of objects as compared to that of the shape and size

of glyph (Ware, 2004). Apart from that, care must also be taken so that the users are not

burdened with non-essential visual complexities in terms of perception and

interpretation. According to Tufte (1997), the design strategy of the interface should be

of the smallest effective difference; i.e. recommends making “all visual distinctions as

subtle as possible, but still clear and effective”. (Tufte, 1997).

Besides, the selection of proper colour scheme, which is quite an important criterion for

encoding valuable information as well as appeal to the interface, poses a subtle challenge

as it may lead to an increase in noise if overused in the interface or the retrieved

information space. As Tufte rightly points out, it might lead to a “visual war with the

heavily encoded information” (Tufte, 2001), if not carefully used. Tuft has suggested

many more graphical and related principles in relation to designing (Tufte, 2001, 1997,

1990), apart from many other authors emphasising the use of proper understanding of

colour as a theory (Ware, 2004; Stone, 2003).

D

Definition: “The use of computer-supported, interactive, visual representations of

abstract data to amplify cognition”

- Card, Mackinley and Shneiderman (1999)



Opponent process theory (Ware, 2004), states that, there is a direct inference of usage

of colour in information systems visualisation. To delve further, according to this theory,

six primary colours are organised (Fig. 3.4)

perceptually as pairs along three axes or

channels: red-green, yellow-blue, black-white

(luminance), which on varying monotonically

across one or more chromatic channels, is said

to produce an ordered colour scale (Ware,

2004, Tuft, 2001). Apart from that, varying the

luminance also helps in creating the

perceptions of depth, motion and form in the

system display.

However, to additionally help in the cognitive assistance through an effective visual

presentation, the ability to have an interactive manipulation with the retrieved visual

data is an intrinsic component of a good information visualisation system (Card et al.,

1999). But to enable the viewers to maintain their control on the system at the same time

ensuing that the system generates essential feedback associated with the user’s actions is

always a challenge. (Benderson and Shneiderman, 2003).

Over the years, there have been several researches carried out on visualisation in the

area of information seeking. A collection of major studies have been grouped as follows:

Author(s) Area of study

Mackinlay et al (1991) 3D interfaces for focussed context views

Robertson et al. (1991) Hierarchy visualisations

Card et al. (1996) Windows layouts

Ahlberg. and Shneiderman (1994)

Visual information seeking – filters with visualisations

Jones (1999) and

Spoerri (1993) Interactive visualisation of query terms

Hearst (1995) Per-result search visualisation – integrating document-query similarities into result lists

Clarkson et al. (2009) ResultMaps – visualised context overview for retrieved results

Wise et al. (1995) Spatialization in representing large document collections

Lee et al. (2009) and Stefaner et al. (2008)

Amalgamating visualisation with faceted dataset navigation.

Fig 3.4: Primary colours as described in

Opponent Process Theory

Table 3.2: Collection of studies on Information Visualisation



3.3.1 Web-based Information Visualisation

Today, there is a growing tendency in the hypertext environment, to cross beyond the

traditional ’10 blue links’ (Broder et al, 2010) and embrace a rather exploratory and

interactive search methodology (White, 2006). But only a few interesting studies have

been carried out in this domain to date. Faceted search navigation (English et al., 2002;

Hearst, 2009) have been introduced to provide an exploratory navigation facility to users

for searching without explicit queries. FacetLens (Lee, 2009) and FacetMap (Smith,

2006), shown in Fig. 3.4 and Fig. 3.6 respectively, are notable researches in this area that

can be said to have lead to the evolution of PivotViewer tool that this dissertation shall

be evaluating.

There may however be a tendency to reach an aspect of cognitive overload due to

complex faceted techniques (Wilson and Schraefel, 2008), but at the same time, there

have been much effort in directing to enhance them with effective visualisations

(Stefaner et al, 2008).

Several studies based on information visualisation have demonstrated that users are

inclined to perceive information and carry out tasks more efficiently if proper

visualisation techniques are incorporated in the system (Carter 1947; Pinker 1990;

Hearst 1995; Xiang et al. 2005). According to Buja and his colleagues, research in the

area of visualisation encompasses two main areas (Buja et al., 1996):

Rendering: involved with construction of visual material (graphs) based on

information, and

Manipulation: iterative process utilised by the users to manipulate the data and

re-render the visualisations (Kumar and Benbasat 2004).

It can be said that these also form some of the core concept of PivotViewer where users

can filter datasets visually.

Fig 3.5: FacetLens interface Fig 3.6: FacetMap interface



Shneiderman also proposed a similar structure that classified graph construction based

on the data for visualisation, which had seven categories depending on the data: one-

dimensional, two-dimensional, three-dimensional, multidimensional, temporal, tree, and

network (Shneiderman, 1996). Besides, Turetken and Sharda also suggested a structure,

especially for the Web, that classified the visualisation depending on dimensions

(Turetken and Sharda, 2007) such as:

Area Author(s) Related work

Semantic content Metadata

Brin and Page (1998); McDonald and Chen (2006)

URL snippets

Salton (1989) Tokenization and indexing algorithms for web documents

Hasan et al. (1995) MultiSurf system – relating search results to search queries

Sebrechts et al. (1999) NIRVE system – 2D and 3D search result representation system

Roussinov and Ramsey (1998); Chen et al. (1998, 2003)

Self-organising map

Turetken and Sharda, (2005, 2004) Fisheye view

Connectivity structure

Munzner (1998);

Mak et al. (2002); Pirolli et al. (2003)

Application of web document connectivity structures

Ben-Shaul et al. (1999) Fetuccino system (IBM) – visualising connectivity of search results

Chen et al. (2002) CI Spider Systems that have directly connected Web documents

Chau et al. (2007) Redips

Mukherjea and Hara (1999) Card-Vis

Metadata

Chernoff (1973) Introduced gylphs – multidimentional visualisations based on metadata.

Chernoff (1973); Scott (1992) Chernoff Faces – representing facial features

Kleiner and Hartigan

(1981); Chuah and Eick (1998); Ebert et al. (1997); Xiong and Donath (1999); Roberts et al.

(2002); Fanea et al. (2005); Forsell et al. (2006); Wiza et al. (2003); Cellary et al. (2004)

Various researches on gylphs

Scott (1992); Rohrer et al. (1998); Sangole and Knopf

(2002)

Visualisations on economic data, weather information,

and text documents

Table 3.3: Categorised visualisation dimensions and related studies



3.3.2 Visualisation of faceted datasets

Over the years, through various studies and research, information visualisation has

slowly emerged into two directions (Smith et al. 2006):

1. Static visualisation – comprises of a set of pre-generated visualisation for a large

data collection.

2. Dynamic visualisation – generation of a visualised result based on user query,

generally on the actual large data collection.

Clustering technique, as discussed earlier, promises to enable effective visualisation

along with an essence of interactivity and manipulation, along with an amalgamation of

introducing a possible 2D or 3D interactivity, such as, the Galaxy of News by Rennison

(1994). Apart from the drawbacks of clustering previously mentioned, its

unpredictability and originality in interactivity, is also an added issue. The dynamically-

clustered, force-directed visualisation was developed by Zhang et al. (2002), which was

however rendered unsuitable for end-user study. ThemeScape (Hetzler et al., 1998),

designed to spatially visualise large document collections by similarity and an analogous

system by Fabrikant (2001), showed that users comprehend an in-depth hierarchy when

they zoom into the spatial information space. Smith et al. (2006) also demonstrated a

similar interface called FacetMap that tried to arrange large collection hierarchical

metadata spatially instead of a clustered document similarity.

Accumulation of an unsorted data collection necessarily abstracts essential details if a

particular axis is chosen, along which the individually grouped items could be collapsed

(Smith et al., 2006). However, studies have suggested methods to scale visualisations to

content heavy databases with lossless compression techniques in information

visualisations (Fekete & Plaisant, 2002; Jerding & Stasko, 1998). But in order to allow

dynamic transformation for user interactions of these techniques, which is limited by

static pre-processing of the individual dataset elements from the system memory, a

native GPU (Graphics Processing Unit) could be exploited (Fekete & Plaisant, 2002).

However, a downside to this as pointed out by Smith et al. (2006) is that, the

visualisation tends to be rather less dynamic with the transferring of visualisation to the

GPU and reducing per-item information from the system.

Besides, there have also been studies on visually representing search queries of large

datasets using the concepts of dynamic queries (Ahlberg et al, 1992), such as Grokker, as

shown in the Fig. 3.7



In most of the above literature, visualisation is very domain-specific, meaning that the

attributes or metadata has to be created in advance and it is difficult to import a new

dataset or facet item (Smith et al, 2006), one exception being Relation Browser++

(Zhang, J., and Marchionini, G., 2005), which presented facets along with their metadata

values and integrated graphics to visualise data distribution, and it could also be

interactively manipulated with mouse-over effects. Amongst the domain-agnostic

visualisation tools, Sportfire (Ahlberg, 1996) and Polaris (Stolte et al, 2002) can be

considered as sophisticated database exploration interfaces, that allow high-end

manipulation and usability through the facets, with the assumption that the users are

expert in their domain.

3.4 User Behaviour – Web Searching

There have been several studies over the years to evaluate user behaviour patterns in

Web search. Most notable studies in this area are:

Author Related Research

Silverstein et al. (1999) Query log analysis of Web search

Spink et al. (2001) Analysis of public and their queries

Jansen and Pooch (2001) Web searching review

Fig 3.7: Grokker - Information visualisation

Table 3.4: User behavioural pattern studies



Such studies have demonstrated numerous behavioural patterns of the users’

capabilities in formulating search queries. Results from such studies have indicated that

users generally tend to carry out queries in one or two short terms and very rarely

formulate queries that contain over three terms (Jansen and Pooch, 2001; Spink et al.

2001). Besides, it should be pointed out that only a rare use advanced searching, yet with

incorrect procedures (Spink et al. 2001). At the same time, users hardly tend to modify

or refine queries (Spink et al. 2001; Silverstein et al., 1999).

Marked by a dearth of inclination to evaluate further search results on the Web, it was

also identified that users rarely considered browsing more than three pages for

obtaining their results. (Spink et al. 2001; Silverstein et al., 1999). Besides, it has also

been found that due to their low tolerance level (Spink et al. 2001), users avoided much

navigation and scrolling and had a higher tendency to look for documents from the top

results in the retrieved lists (Kobayashi and Takeda, 2000). In addition, Yang (2005)

concluded through his studies, that users generally tend to expect immediate answers

using minimum effort. However, he did not expand on why they demonstrated such

behaviour. Much of it could be attributed to the lack of interactivity of retrieval process

and visualisation of the search results.

Besides, current search interfaces also lack the ability to help users in vague situations,

with no proper assistance in articulating their needs and directing towards a solution

(Marchionini, 1992). Ideally, there should be a higher cognitive search burden on the

system rather than the user (yang, 2005) and researchers have predicted the possibility

of developing even better interfaces over the coming years, in visually interactive

information search interfaces (Kobayashi and Takeda, 2000).

According to Ferrara (2008), a Web and information architect with over a decade of

experience, the user behaviour in information searching could be attributed to the

following six categories:

1. Domain expertise – Users having the familiarity in a subject are said to have a

better ‘verbiage’ or expertise to search for information in that particular domain.

Hšlscher & Strube (2000) has studied this characteristic through a Web search

behaviour and Bhavanani (2002), through his domain specific studies on users’

have thrown a further light on this.

2. Search experience - Search experiences and technicalities of using search

refinements (e.g. - using Boolean operators), have also indicated to play a vital

role, which is however not essential. Ferrara points out that, those with lower

technical knowledge of searching but greater domain knowledge can also obtain

results easily, as compared to technophiles having lower domain knowledge

(Ferrara, 2008).



3. Cognitive style- The trends in gathering new information, also has an influence

in the way users explore information. Researchers such as Nigel et al. (2002)

have proposed several cognitive styles and associated them with schemas.

However, Ferrara (2008) has suggested the existence of the spectrum comprising

of:

a. Global thinkers – who tend to have a broader overview and

b. Analytical thinkers – those who have a narrowed down search approach

on a particular criterion.

To further support this, White and Drucker (2007) also indicate that users

generally tend to leverage themselves somewhere within the extremes of the

above spectrums, but mostly with a closer inclination to either of them.

4. Global type –Broader (2002) has suggested that search patterns can also be

categorised according to their goals:

a. Navigational Search- which seeks location oriented data.

b. Informational Search – which seeks documents on particular domain.

c. Transactional Search – which fulfils the user to accomplish an online

activity.

5. Mode of seeking – When users use an information retrieval interface, situations

can be primarily be of the following categories (Rosenfeld & Morville, 2002;

Spencer, 2006):

a. Searching for a known item – when users are aware of exactly what to

extract or look for from the information space;

b. Exploratory searches – where users have a broad awareness of what they

want and not any precise set of criterion;

c. Vague or when users are unaware and have no idea of what to look for.

(expressed in further details by Bates, 1989 on her ’berrypicking’ article).

6. Situational Idiosyncrasies - Ferrara (2002) adds a penultimate layer of

unpredictability in user search behaviour, by suggesting that a similar search by

a particular can have contrasting results depending on two or more different

occasions depending on the idiosyncrasies of the user. According to him, users

tend to respond with a different search approach depending on the situations for

retrieving information from a particular search interface, e.g.- a user on a

leisurely mood will tend to have a more casual approach and end result or

information through their search on a particular topic, as compared to another

given time if the situation was quite pressing such as an approaching deadline.



3.5 Summary

In this chapter information retrieval techniques have been thoroughly discussed with

relation to major works done in this area. Particular focus was highlighted upon

explaining the studies carried out on traditional information retrieval systems and Web-

based information searching. Special attention was also given, to carefully study the

findings in the area of information retrieval of large faceted datasets due to its close

association with the technology employed in PivotViewer. Apart from this, a brief

discussion of Web user interface has also been carried out. Besides, studies related to

information visualisation was also thoroughly evaluated with special focus and

discussions made in the area of Web-based information visualisation and visualisation of

faceted-datasets. Finally, user behaviour in Web searching has been briefly indicated,

along with an overall mention of related studies by researchers, denoted in tabular form

for certain topics.



Chapter 4

4.1 Introduction

PivotViewer (or simply Pivot) is a product of Microsoft that works in the Silverlight 4

framework as a control, when hosted inside a Web script. It is said to allow the users, to

visualise and explore large collections of data, in an interactive way. It uses a Web app

framework or a plug-in, which also has an API that can pass data between the control

and a hosting website.

According to a demonstration by Microsoft Silverlight Team (2010), this control is a

“multifaceted multimedia experience of a data set that provides an opportunity to look at

trends within the data, and kind of observe larger forces at work and drill down into

specifics, and then formulate hypothesis, so that users can move from the data to

something closer to knowledge”.

Pivot uses Microsoft Deep Zoom technology in a Silverlight interface, to browse through

collections of data, by allowing the users to zoom in and out of the collection, as well as,

change the facets of the metadata, that the user is interested in, filter out sets and bring

them back in and analyse the individual items, while all the time having a broader

picture. It allows users to sort, organise and categorise data dynamically. Developers can

either implement the full application experience, that takes advantage of the Pivot or

they can embed just a frame of the

pivot control into an existing

website, with a customisable

control, that can obtain all the

technology required to consume and

interact with pivot collections, using

Silverlight. In this Chapter, the

technology of PivotViewer is

discussed in details.

MICROSOFT PIVOTVIEWER

Fig: 4.1: Example of a Pivot Interface



4.2. PivotViewer Collection

As defined by the Microsoft Silverlight Team (2011), the collections in Pivot is said to be

composed of two parts:

CXML – Collection XML Schema – which is an XML file (with a “.cxml” extension)

that contains the detailed descriptions of the items in the collection, and

Collection Image Content – A set of arranged images in a Deep Zoom format

There are three discrete steps to create a Pivot collection:

1. Data collection – A set of data needs to be collected, that is to be turned into a

dataset and represented. [More details in Section 4.3-Collection Design]

2. Creating CXML and Image rendering – After the data sources have been decided,

it needs to be formulated into CXML (or Collection XML). The images will also need

to be transformed into a Deep Zoom Format. There are a variety of tools to achieve

this, from MS Excel Pivot plug-in to open source libraries. [More details in Section

4.2.1 for CXML schema, Section 4.2.2 for Collection Image content ]

3. Collection Hosting – After creating or building the collection, it needs to be hosted

into a server according to accessibility requirements.

Pivot Architecture

A collection in Pivot is similar to any particular Web content. The set of relevant files on

the server is accessed by the local client, which understands the format it needs to be

displayed. In traditional interfaces, these files are generally HTML and images (apart

from any other scripts). However, in Pivot, these files are CXML and Deep Zoom

Collection (DZC) images. Users can access this collection from the hosted web page,

where the PivotViewer displays the content, using Silverlight Control.

Fig 4.2: Pivot Architecture



4.2.1 Collection XML Schema (CXML)

CXML or Collection XML is the main data structure or in higher level, a collection of

property or value pairings, of the data/image collections that attributes to the experience

provided within the PivotViewer. Use of categories as filters in the search interface of

PivotViewer is perhaps the most important and underlying characteristics of

PivotViewer. This is achieved through ‘facets’ that can be grouped into ‘facet

categories’.

A simple example of a CXML file is provided below which results in the visualisation of a

single data:

The above code results in the visualisation of a single data in the interface as shown

below. (Note: This need to be run either through the PivotViewer Application Tool

provided by Microsoft Live Labs or via an ASP.NET server – which shall be discussed in

details shortly in the Chapter 5 - Methodology). Each section of the above code

mentioned above has been dissected and presented describing their functionalities:

<?xmlversion="1.0"?>

<CollectionName="Hello World Collection"

SchemaVersion="1.0"

xmlns="http://schemas.microsoft.com/collection/metadata/2009"

xmlns:p="http://schemas.microsoft.com/livelabs/pivot/collection/2009"

xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"

xmlns:xsd="http://www.w3.org/2001/XMLSchema">

<FacetCategories>

<FacetCategory Name="Hello World Facet Category One"

Type="String"/>

</FacetCategories>

<Items ImgBase="helloworld.dzc">

<Item Img="#0" Id="0"

Href="http://www.getpivot.com"Name="Hello World!">

<Description>This is the only item in the

collection.</Description>

<Facets>

<Facet Name="Hello World Facet Category One">

<String Value="Hello World Facet

Value"/>

</Facet>

</Facets>

</Item>

</Items>

</Collection>

Source: Microsoft Silverlight Team (2011)

Fig 4.3: Simple example of a CXML file



4.2.1.1 Structure of schema

The collection of PivotViewer is a combination of a set of image collection associated

with a CXML file. The beginning of the CXML file comprises of a top-level node called

Collection, which defines the overall properties of all the elements in the set of

collection.

The Collection node has two main children which has further sub-children (Microsoft

Silverlight Team, 2011):

1. FacetCategories - This is one of the immediate children of the Collection

container. Every facet linked with an item should have an associated face

category. It consists of the facet name, string format, visibility options and type of

the facet, which may either be Number, String, LongString, DateTime or Link.

Fig 4.4: Simple Pivot Interface based on the previous sample CXML code



2. Items – it contains the definition of every individual item in the collection and

thus contains most of the information related to the collection in the file. Each

item constitutes of the following properties:

a. Href – a link that redirects from a double-click

b. Name – a name given to the item

c. Description – description associated with the items

d. Id – an identifier associated with the image or information

e. Img – the image asset

f. Any other custom facets or properties that might be necessary to include.

However, in this connection it must be noted that all the above properties are

always not necessary and can be omitted if necessary, e.g. - we may not want the

users to double-click on the picture and get redirected to a link and hence we

may completely remove the Href property.

Fig 4.5: Structure of schema



4.2.2 Collection Image Content

One of the most significant characteristics of PivotViewer is the use of images through a

technique called Deep Zoom format. In the initial section, an overview of Deep Zoom

Images and Collections are discussed, followed by a quick guide on how to build such a

collection for PivotViewer. The later sections will cover information regarding the tools

and technologies that can be used in order to author the images, the encoding

considerations for the images, and finally on how to deploy the deep zoom images on

production servers.

4.2.2.1 Deep Zoom Image Collections

Overview

The primary form of display of images in PivotViewer consists of a Deep Zoom Collection

format that comprises of individual Deep Zoom images. In order to achieve this, the large

dataset set represented by the collection of random sized images are pre-rendered and

tiled to allow a progressive access. There are various tools and techniques that can

render the images to this format.

The individual images are encoded into a set of Deep Zoom Image (DZI) tiled pyramid.

These sets of images are then encoded into a collection called Deep Zoom Collection

(DZC) and are represented in a layout as described by MSDN called ‘Morton Layout’.

More information about Deep Zoom file format and Morton Layout can be

obtained at: http://msdn.microsoft.com/en-us/library/cc645077(VS.95).aspx

Fig 4.6: DZI – DZC based on Morton Layout



The overview of file organisation of DZI and DZC are briefly discussed below:

Format Overview of file organisation

Deep Zoom Image (DZI)

1. XML file containing details of the collection.

2. Subdirectory containing multiple subdirectories having

the images pre-rendered in various resolutions. It

contains the same name as the XML file followed by

“_files”, e.g. – if the XML file name was abc.xml, the name

of the subdirectory will be abc_files.

Deep Zoom Collection (DZC)

1. XML file containing details of individual DZI images in

the collection.

2. A subdirectory containing the DZI images with the

naming structure similar as above.

Sparse Images

PivotViewer also has additional support for sparse images,

i.e. the images that has varied resolutions in various areas,

which can be said to have been generated by forming

multiple images at varied scales of resolution.

DZI and DZC content creation

There are several methods to create the Deep Zoom images for PivotViewer collection.

However, the suitability of each method is dependent on whether the collection or the

production is either a onetime build or part of a continuous or dynamic work flow. A

quick summary of the procedure is provided below:

Procedure Description

Pivot Collection Tool – MS Excel Plug-in

This is perhaps the simplest procedure to generate pivot

collections from scratch. The Excel spreadsheet is used to

store the facet information in the form of separate

columns. A column contains all the information of the

source of all the images that need to be generated

corresponding to the other associated fields. DZIs and

DZC can be automatically published and created using

this plug-in.

Manual Deep Zoom creation

CXML file can be manually created from the data

available in any particular dataset by using tools to

export it in XML. This could be achieved by using either

Deep Zoom Composer or Deep Zoom command line

utility, which can be downloaded freely. Both the above

tools can also be automated using shell scripting.

Custom Applications

In order to accommodate a particular domain of

workflow, custom applications can also be used. In order

to achieve this, the Deep Zoom Tools Library can be used

to create DZI and DZC as Windows application.

Table 4.1: Organisation of DZI and DZC

Table 4.2: Summary of procedures to build Pivot Collection



Image Compression Guidelines

The formats for the images that can be used to create the DZI and DZC collections can be

of JPEG or PNG formats. However, each of these formats have their associated

advantages and disadvantages and hence it should be appropriately chosen, based on the

system and the nature of work flow, such that an optimised experience is achieved.

Hence a proper balance must be maintained between in the compression or the quality

of the images based on the processing speed of the server. It should be noted while

adjusting the image settings, that an increase in the compression can improve the

performance of the interface but at the same time it can also degrade their quality.

Based on the formats, here is a comparative chart between JPEG and PNG formats:

JPG PNG

Compression Lossy Lossless

Continuous

tone images

Provides efficient

compression

Provides inefficient

compression

Suitability Best for web based images

Appropriate for simple

diagrams, texts, single

colour images

Alpha channel Has no support

Supports alpha channel

meaning it can support

images with transparency

Quality Lower compared to source Comparable to source

Image size Smaller Larger (alpha channel

increases it even further)

Experience Faster due to lower

bandwidth usage

Slower due to higher

bandwidth usage.

While both the above formats have their respective advantages and disadvantages, it is

always advisable to experiment with a set of collection and carefully evaluating with the

system and nature of the content available. Compression parameters can be adjusted to

accommodate suitability with the system and keeping in mind with the quality of the

images obtained. There are no rules in this regard and the optimal configuration can only

be achieved through experimentation based on the efficiencies of the system.

However, it must be mentioned here, that for maximum compatibility and efficiency,

consistency must be maintained in the format of the images used for the particular

collection.

Table 4.3: Comparative chart between JPEG and PNG formats



4.2.2.2 Deploying Deep Zoom Collections

Overview

There are no special Web services which are separately required for deploying Deep

Zoom Collections. The XML file along with the associated image files in the sub-

directories can be accessed using traditional HTTP protocols.

Working with several images based on Deep Zoom Collections can lead to high volumes

of files and sub-directories that are necessary to generate the tile pyramids. This can

attribute to the total space necessary to accommodate all the files at a much higher size

than the actual source. As such, working with thousands of files can prove to be quite

challenging and hence there are some suggested techniques by Microsoft Silverlight

Team to help deploy Deep Zoom Collection to a web server:

1. In place generation: This technique suggests in rendering the Deep Zoom

Images(DZI) and Deep Zoom Collection(DZC) directly into the server that

contains the set of image contents, thus avoiding the hassle in transferring them

to another hosted location. Hence it is advantageous as a lot of time can be saved

in preventing the duplication of large set of files and sub-directories. However, it

may not be feasible in scenarios where the production server provides no

support for the execution of rendering process, for proper verification before a

final deployment.

2. Content Reduction: The content at the top position of the DZI pyramid, which is

also included in the DZC pyramid, is often duplicated. As such, for every DZI

pyramid, the files and sub-directories that make the tip can be removed. For each

content present in the collection, a maximum of nine files and directories can be

deleted. This significantly reduces the total size as well as number of images. At

the same time, several tools used to create Deep Zoom contents also allow in

configuring image dimensions, which can be used to set up proper image sizes

and effectively reduce some memory.

3. Creating ZIP: Custom generators that are used to build contents can be used to

render DZI and DZC directly into ZIP files instead of individual images and

directories. This significantly reduces the time required to deploy a single or a

few ZIP files, as compared to hundreds and thousands of individual directories

and files. However, it should also be noted that, it is also required to un-ZIP the

files into the appropriate directory formation on the production server to

facilitate the interface to deploy the collection.



Updating Collections

Users are able to view the Pivot Collections in the browser through the embedded CXML

within the body of the website, which is cached by the application in the server. As a

result, any updates directly on the collection present the server on collection with which

the CXML is linked, will only invalidate the collection and throw exceptions, creating

inconsistencies in the results obtained.

Hence, to properly update a Deep Zoom Collection, a new collection has to be generated.

This collection should then be deployed at a different URL, but in this connection, it must

be also mentioned that the URL should contain some identifier that can distinguish it

uniquely, such as version number or timestamp. This new URL can then be hosted into

the webpage and updated. The URL indicating the CXML file may remain the same, only

the URL inside the CXML file that points to the collection needs to be updated. This

procedure ensures that the users who are currently viewing the Pivot collection page are

not disrupted, but at the same time, new visitors are able to interact with the updated

collection.

If there are new Deep Zoom image files deployed and no other changes made for any of

the other individual Deep Zoom images, updating only the Deep Zoom collection will

make the necessary changes instead of re-rendering the whole collection and deploying

it. This also enables the PivotViewer to reuse other images in the local cache of the

system, which were rendered in Deep Zoom format previously.

Caching Collections

Though the web server treats the Deep Zoom collection as a set of static items, an

optimal efficiency can be obtained by active caching. This can be achieved by allowing

the DZC an unlimited cache, which will ensure significant reduction of multiple content

access permissions along with arbitrary proxy servers, by serving the requests via the

caches, also enabling load reduction in server at minimal bandwidth costs.



4.3 Collection Design

The challenge in designing a large collection is in deciding the technique to present the

entire information collection at once. They prime factors in designing a proper set can be

segregated into three steps:

Collecting data or subject matter with quality details and/or images

Deciding on the categories to create filters on

Linking the relevant contents

A proper design comes with several iterations starting from a simple set to gradually

increasing in complexity.

The steps in this process are discussed below:

1. Define the Subject Matter: As an initial step, the theme or topic of the collection

needs to be decided. It should be kept in mind to include such a subject, so that

users would be keen to explore more, instead of just looking for a particular data.

This is necessary to lead the users into discovering trends or patterns from the

vivid views, across all the contents.

2. Define the User: Secondly, users should be defined of the collection requirements

according to the needs. For example, a photograph enthusiast would look for

different contents in a camera collection than someone who is just shopping for a

camera.

3. Define the Key Tasks: Thirdly, the key tasks, whether it is goal oriented or general

browsing, that shall be carried out by the users in the interface, should be defined

and specified. The important views and information that should be accessible to the

users for viewing should also be decided.

4. Choose Content and Data: Fourthly, the facet categories, details to be included in

the info panel, general view etc. which will allow the users to carry out those tasks

need to be decided.

5. Evaluate and Iterate: Lastly, the collection should be experimented on users for a

proper evaluation. Previews can be used to explore and experiment with the

collections, and the tasks defined in step three should be carried out with the

contents decided in step four. Further iterations should be carried out from step

four, in case the tasks are difficult to achieve.

Fig 4.7: Iterative process in designing



4.3.1 Kinds of Collections

Collections can be categorised into three primary categories according to their size and

behaviour to customer queries. They can be either composed of a static (or pre

generated) XML or a dynamic XML, based on the user query. The following table aims to

distinguish the collections categorically.

Simple Collections Linked Collections Just in Time Collections

Implementation: Easy

Size: Up to 3,000 items

Implementation: Medium

Size: Limited by storage complexity

Implementation: Difficult

Size: Unbounded

Details:

A very common

collection

Comprises of static

data collection which

is loaded at one time.

Contains static visuals

contained in one Deep

Zoom Collection

Details:

Frequently used for

collections with

thousands of contents.

Comprises of inter-

linked simple collections

Consists of static data,

which is loaded per

simple collection at one

instance of time.

Contains static visuals

contained in multiple

Deep Zoom Collection

Details:

Used generally for very

large datasets

comprising of sets of

nearly and over a

hundred thousand

contents.

Comprises of dynamic

data, which is retrieved

dynamically on queries.

Contains partially

dynamic visuals

contained in dynamic

Deep Zoom Collection

Table 4.4: Kinds of Collections



4.3.2 Facets and Facet Categories

Having already chosen the theme or subject matter for the collection, the next significant

step in the collection design is selecting the facets and the facet categories to implement,

and deciding where to put them on display. Facets signify the data or value of a content

or item, and the facet categories are a collection of facets under a particular category

name. For example: If a collection consists of facet categories called “Country”, then

“India”, “UK” etc. might be some of the facets in that category. There can be several forms

in which facets might be present in PivotViewer. Generally, they are presented as filters,

part of information in the information panel, and/or links associated to an item. [Details

have already been covered in Section 4.2.1 on CXML, about facet categories and facets]

The following diagram depicts how facets and facet categories are displayed in Pivot:

Some of the important points to note while designing facet categories (as devised by MS

Silverlight Team, 2010) are:

1. Naming and formatting – Facet categories should be appropriately named,

formatted and ordered to optimise readability and comprehension. (For

details, Facet Naming and Formatting sections in Sections 3.4.5 and 3.4.4 can be

followed).

2. Presence as a filter – It can be chosen, as to which category to include in

filter panel. All categories in filter panels can also sort their individual facet

entities.

3. Presence in the info panel – It can be chosen, as to which category to

include in the information panel as a part of all the metadata associated with

the content.

4. Keyword Filtering – By allowing keyword filtering, users can enter

keywords to be used as filters while querying. It should however be noted

that only facet categories that assist in identifying the contents are included

in keyword filtering, instead of rather broad or tangential properties.

Fig 4.8: Facets and Facet Categories



4.3.3 PivotViewer Interface Features

There are a few important features in the PivotViewer interface that should be briefly

introduced and discussed to help arrange in organising facet categories properly:

Filter Panel

This serves as the primary area which allows the users to explore the facets and facet

categories to slice and dice the collections to narrow down their search criteria, by

forming combinations (refer Fig. 4.8).

Info Panel

This panel consists of textual information related to the image content and is visible

when the user either selects or zooms in on the picture. It plays two important roles:

Furnishing Details – It acts as a container for the detailed information associated

with the contents, and allows the users to explore the details about the facet they are

presently viewing.

Encouraging Exploration – It also contains related links that assists the users to

discover similar contents or navigate away to another content or set of contents.

Title: It displays the name of the content or item.

Description: This field contains additional details about the item and is visible above the

properties section of the info panel.

Properties: This section consists of the facets associated with the related item, or the

facet categories that it is a part of. There are three distinct types of texts that can be

displayed here:

Fig 4.9: Information Panel- I



Filters – These are the texts, when clicked, filter the collection to show all the

items that belong to the same facets.

Static Text – this is used to generally provide further information or text about

the items that are not necessarily required to be a part of the filter panel.

Links - These comprise of general hyperlinks, which may be used for constructing

linked collections.

The figure gives a further reference (Fig. 4.10).

For certain cases, it may be required to visualise the facet categories discreetly in the info

panel as compared the filter panel. For example, a situation may arise where a different

format of data may be used in each of them, though the actual data may be the same. In

such cases, two facet categories may be created: one that can appear in the info panel

and the other that can appear in the filter panel.

Other Links

The bottom most section of the info panel contains an array of links associated with the

content. These are:

Links Related to Collections – These consist of the links that are related to the

collection items in some way. These are fetched directly from the web based on

the facets of the content selected.

Notices and Copyright Information Links – This consists of the copyright

attribution text, which can be provided as a link that may be specified in the

CXML. This attribution text appears at the very bottom of the item info panel.

Fig 4.10: Information Panel - II



4.3.4 Facet Naming Facets and facet category names should be concise and clear, so that they don’t get

truncated in common usage (Microsoft Silverlight Team, 2010). Hence, below are some

of the guidelines that can be followed to achieve this:

Capitalization –Headline or title style conventions for capitalization may be

followed. However, facet values are case-sensitive, and hence the values should

also use consistent casing.

Plurality - singular form of the category names may be used. For example: Rather

than naming "Locations," for a category name, "Location" could be used.

Brevity and Redundancy – To name the categories, a few words should be used

as possible to name the categories. Unnecessary long phrases should be avoided,

keeping in mind that the users are already navigating within the context of the

collection that may be inherently implied in the context.

Acronyms – Use of acronyms may add confusion to certain sets of users and

hence may be avoided to prevent ambiguity and confusion.

Numbers – Direct use of Arabic numerals ("1, 2, 3..” etc) should be used, rather

than writing them out (e.g. "one, two, three"), for quick visibility and proper

legibility.

String vs. LongString – By using LongString, multiple textual lines can be

wrapped onto multiple lines. This should however, be used only for non-filtered

textual information, and which are longer than a number of words. String format

should be used for all other purposes.

Sorting Behaviour – Sorting of facets in categories can be carried out, based on

alphabetical order or according to the quantity of available items in each facet.



4.3.5 Facet Values and Formatting

In order to have a proper exploration experience, the following should be kept while

creating and formatting facet values:

Single Item Values – categories that may have the probability of having a very

large number of facets or those that may have only a few items in each facet

should be avoided to provide a better exploratory experience. The pictures below

provide an illustration (Fig. 4.11):

Compound Category – Situations may arise when it is necessary to create a

‘compound category’, in order to display multiple values together in the info

panel, as well as making useful filters. In order to do this, separate filterable

facets may be created individually, and then add another separate facet (non-

filterable) corresponding to those values.

Distribution for Interest – the facet categories should be investigated as to how

they are displayed in the interface. If certain facets have no or very less values and

there are gaps in the view, they may be omitted to provide a better experience.

Creating compound categories as mentioned above may also be considered to

optimise the results in the view.

Consistent Weights and Measures – a level of consistency should be maintained

across the collections while naming units.

Currency – Facet values representing currency values should use appropriate

currency formats (e.g. Formatting the column to represent currency).

Number Formatting – Numbers should be formatted according to the needs with

the appropriate decimal places (e.g. Year should have no decimals).

Units – Units should be included in the appropriate number formats instead of

facet names.

Fig 4.11: Guidelines for facet values



4.3.6 Imagery

One of the prime features of PivotViewer is the visual experience through the large set of

imagery. Below are some of the suggestions that may be incorporated to produce an

optimal visual experience, as provided by Microsoft Silverlight Team (2010):

High Image Quality –recommends using images of at least 500px, however it

may be suggested to use the best quality images as possible, since the images are

converted into Deep Zoom format and there are no extra delays caused after all

the images have been rendered.

Consistent Aspect Ratio - In order to have a uniform, dense and regular display

of the images, a level of consistency should be maintained in terms of the aspect

ratios chosen, by ensuring that all the images are pruned to a similar ratio.

Source Image Format – Though not essential, but it is recommended that the

images are in JPG format, with a low compression level (maintaining a quality

level of 85%-95%), before converting to Deep Zoom format.

Edge Treatments – Image sets having very light colours may be given darker

edges to ensure a higher contrast with the PivotViewer interface background.

Enhanced Collection Imagery – In order to enhance the visual power of the

images, additional graphics and colour schemes may be added to deliver a better

visual experience of the contents. Graphical additions may help in visualising the

statistics and/or add useful labels providing in-context labels. This information

may help the users to overview multiple contents faster, instead of viewing the

info panel for the details each time. Hence, adding colour schemes and graphics

may enable the users to scan for particular type of contents easily, at the same

time, also provide additional information when zoomed in.

Fig 4.12: Example for enhanced collection imagery



4.4. Summary

In this chapter a brief introduction about Microsoft PivotViewer is provided initially. This

was followed by several concepts on the technicalities associated with the architecture

and creating collections with this interface has been elaborated. To achieve this, the

concept of PivotViewer Collection was explained with an introduction to Collection XML

Schema and the structure of the schema. Along with this, the collection image content

was explained highlighting two key associated technologies – Deep Zoom Image

Collection and deploying the Deep Zoom collections. Next, designing the Pivot collection

was explained through insights on the kinds of collections, the concept of facets and facet

categories and steps how to organise the facet categories in PivotViewer. Apart from this,

guidelines on facet naming and key factors in providing suitable facet values and

formatting have been explained. Finally, it has been concluded with a brief discussion

and guidelines on the imagery that should be used in PivotViewer



Chapter 5

This chapter discusses the procedures undertaken, to carry out the practical

implementation of a PivotViewer interface, based on a historic data collection. Detailed

descriptions of the evaluation mythologies undertaken in this research, highlighting the

related literature are discussed. This is followed by brief explanations, on the types of

user experiments undertaken to evaluate the Pivot interface designed, as well as other

pre-existing systems in such a way so that a complete overview of the present systems

are obtained along with what the users feel about PivotViewer. A careful approach has

been taken to obtain a holistic yet detailed data from the experiments, such that it can be

carefully analysed and evaluated as to what the users actually think about the Web based

search interfaces and information retrieval systems that currently exist in the market

and how they compare them with the Pivot interface, released by Microsoft.

5.1 Literature Review

According to Plaisant (2004), carrying out evaluations on information visualisation

always has a challenge. These challenges are carefully noted down while crafting out the

experimentations and laying out the questions for evaluations. Besides, a study of over

fifty users on information visualisation systems (Komlodi et al., 2004), showed that there

are four thematic areas that could be employed for optimal evaluation:

1. Controlled experiments that compared design elements – such as studies on

widgets (Ahlberg and Shneiderman, 1994b), studies to relate information to

geographical display (Irani and Ware, 2003), etc.

2. Usability evaluation – for example studies on the problems users encountered

with interface tools and how designers refined them (Sutcliffe et al., 2000; Byrd,

1999).

RESEARCH METHODOLOGY



3. Controlled experiments that compared tools – often a common study, mostly

comparing novel technology with pre-existing or state of art technique (Plaisant

et al., 2002).

4. Case studies – though time consuming, these enable users to carry out real tasks

in a natural environment, thus reflecting on the feasibility and usefulness in

context of the scenarios (Trafton et al., 2000).

The above themes are carefully considered and incorporated in the experimentation that

has been set up for evaluating the systems. Apart from this, several other techniques of

evaluation have also been studied to provide an insight into the best evaluation

methodologies that can be adopted in this research to bring out an effective and robust

result. Some of them include:

Author(s) Related study

Munzner et al. (2003) TreeJuxtaposer – task conduction and interpretation of results

Hong et al. (2003) Zoomology – custom design for a single set of data leading to a useful evaluation tool

Spenke and Beilken (2000) InfoZoom – tool to manipulate tables

Auber et al. (2003) EVAT – another powerful analytical tool

Morse et al. (2003) Taxnote – emphasising evaluating the importance of labelling of the retrieved displayed information.

Sheth (2003) Demonstrated the benefits of combining several tools

Apart from this, the ImageCLEF studies (Clough et al., 2004; Clough and Sanderson,

2003) have also been carefully studied and followed to obtain a deeper understanding of

suitable evaluation techniques that might be useful to incorporate in this project. Finally,

a few piece of previous dissertation and doctoral thesis works on image retrieval,

information retrieval and visualisation have also been closely studied (Hoeber, 2007;

Read, 2007; Yang, 2002) to get an insight into the difficulties faced as well the

effectiveness of their methodologies in evaluation in this domain of study and a careful

attention to detail has been considered in creating the user experiments as well the

questionnaire. A formative technique has been adopted for the purpose of the study.

Table 5.1: Evaluation Methodologies carried out in previous studies



5.2 Dataset (St Andrews University Library Photographs)

In order to carry out a proper evaluation of the PivotViewer, not only a large collection of

images was needed, but also a collection that had quality captions to facilitate proper

textual retrieval and faceted filtering. Besides, to accommodate such a study based on

huge number of pictures, copyright issues were also to be considered as the digitisation

of images today meant that they have high market values and it would restrict proper

distribution in case further research work is carried out in this domain in future. Hence,

taking all these accountabilities into consideration, a historic collection of St. Andrews

University Library was used. The Information School in the University of Sheffield,

already has research links with the library at St. Andrews University, which currently has

an archive of one of the largest historic collection of images, of over 300,000 pictures,

taken by some of the famous Scottish photographers (Reid, 1999). A cross-section of

around 28,133 images has already been used as a part of a digitisation project in the

CLEF studies carried out at the University of Sheffield.

Given the scope of this research, the local resources available and considering the time

constraints for the overall evaluation, out of this collection, a set of 10,000 images (with a

resolution of approximately 368x234 pixels each) was used for the purpose of

PivotViewer in this project. Due to the historic nature of the collection, a high number of

images (around 82%) from the original collection were in greyscale, and belonged to the

year range between 1832 and 1992.

It should be mentioned here, that this historic collection was considered for the project

as it represented a realistic archive of images with high quality captions. Besides, the

metadata associated with the images also contain a varying set of information, on a range

of topics, which would prove to be challenging for both image as well as textual retrieval

through the visualisation power of PivotViewer.

It should be noted that full permission to use the image collection was granted by Dr.

Paul Clough, who was a lead researcher of CLEF on the St. Andrews collection, in the

University of Sheffield, and hence there are no associated copyright issues in this piece of

dissertation.



5.3 Overview of metadata

The vital metadata of the images was distributed into text documents with the same

filename as that of the images and had 8 different sections (shown in Fig. 5.1 below):

Title – It contains the title of the picture explaining the picture in a short

sentence.

Short title – It’s a shorter version of the above title [often similar or exactly same

as the title].

Location – contains the information regarding the location of where the

photograph relates to, in terms of its city and country [often either cities or only

country name present].

Description – contains the description of the photograph in further details.

Date – the date the photograph was taken on.

Photographer (or Originator) – the name of the photographer(s) or the

photography company from where the photograph has been obtained.

Categories – the related subject or tags that the photograph can be said to be

related to.

Notes – any other details or notes about the photographs.

Fig 5.1: Example of metadata associated with images in St. Andrews

University Library historic photographic collection



5.4 Building the Pivot Collection

This section will briefly discuss the procedures taken to implement the photographic

collection using the PivotViewer tool for the dataset chosen and discussed above.

System requirements for executing and building PivotViewer applications:

System Microsoft Windows Vista/7 (Aero enabled),

2-GHz 32-bit(x86)/64-bit(x64) processor,

2GB RAM.

Graphics Memory 256 MB of video memory (a dedicated GPU is

recommended) [Note: Pivot does not support most

Intel integrated video chipsets].

For application

execution

.NET Framework 3.5 SP1 (or higher)

Internet Explorer 8 (or above)/Firefox/Chrome

For Application

building and testing

Visual Studio 2010

Silverlight 4 Tools for Visual Studio 2010

Silverlight 4 Toolkit (released April 2010)

PivotViewer Control

Techniques for creating Deep Zoom content for Pivot was discussed in the previous

chapter. For the purpose of building the CXML file for the current St. Andrews data set

and generate its Deep Zoom image collections for purpose of this dissertation, Microsoft

Pivot Collection Excel add-in is used. In short, this tool allows developers to create the

Pivot collection by creating the individual facet categories for the overall collection and

then filling in the spreadsheet with the corresponding metadata for each of the

individual items, which can then be published and exported to required Deep Zoom

format understood by the Pivot application.

Table 5.2: System requirements for executing and building PivotViewer

applications



After the Excel add-in has been installed, a new ribbon called the Pivot Collections will

be added. There are three special components in this tool as briefly described below:

Excel Ribbon Tab or “Pivot Collections” Tab - the main control area of the

collection, that is used to associate images with their related metadata, create

collection-wide properties, test and publish or render the entire collection.

“Collection Items” and “Collection Properties” Worksheets – These

worksheets (visible as tabs at the bottom) harbour the data of the collections and

properties associated with the whole collection, respectively.

Excel Table

Rows– correspond to a particular item in the collection.

Columns– correspond to a particular facet category

Cells– contain the item’s value (or facet)

As a first step to start building the collection, the option to create a New Collection

should be clicked (Shown in Fig. 5.2 below)

This will create a new workbook with the following default columns:

Image Location- Contains the path of source image file.

Name- Contains the name of the particular item, which is displayed at the top of

the info panel in PivotViewer.

Href- A URL that can be associated with this item which will redirect the user to

the link provided if the item is clicked in the PivotViewer interface.

Description- Contains the description of the related item.

Preview – provides an automatic thumbnail of the image provided at the ‘image

location’ column.

Fig 5.2: First step to start building the collection



For the purpose of the St. Andrews collection, the Href column has been omitted since

there was no further requirement for navigating to a web URL through the images. The

preview column was also deleted as it had no affect on the front-end of the system and

besides, it was taking extra resources as well as time, to generate the pictures, thus

delaying the process. In addition, six new columns representing the facet categories of

the St. Andrews University Library were added to accommodate the metadata:

Record ID Title Year Month Originator Notes Category

The corresponding details of the collection set were then filled in.

[Note: given the magnitude of data, scripting was used to gather the data from the

individual text files that contained the metadata for the individual images into a separate

excel sheet which was then transferred in the excel Pivot sheet]

Adjusting Visibility of Categories

By default, when new fields are added, the facet categories automatically appear in both

the filter panel and the info panel of the PivotViewer. Hence, the “Category Properties”

panel in the tab is used to modify visibility for each of the facet categories.

Fig 5.3: Columns representing facet categories for St. Andrews collection



The fields can have the following modification options:

Filter Panel – This enables the images to be filtered in the PivotViewer according

to the elements in the field of the chosen column as facet categories.

Info Panel – Gives an option for the current category to appear in the info panel.

Keyword filtering – Allows the elements in the field for keyword filtering.

Keyword filtering enables the viewer to provide keywords to apply as filters in

the collection.

In the St. Andrews collection, the following setting have been incorporated

Categories Filter Panel Info Panel Keyword filtering

Image Location Name

Record – ID ✓

Title ✓ ✓

Year ✓ ✓ ✓

Month ✓ ✓ ✓

Originator ✓ ✓ ✓

Location ✓ ✓ ✓

Notes ✓

Category ✓ ✓ ✓

Description

Miscellaneous Property Adjustments

Categories Requirement Adjustment of property

Year Numeric facet The entire column should be formatted to change the ‘Format Cells’ property to number with decimal places to 0

Category Multiple Values To accommodate multiple values as filter options within the same facet category, the values should be separated individually by ‘||’

Notes LongStrings

Since the notes generally contain long set of textual data, these are generally needed to be set to equivalent LongString format. To achieve this, the column should be formatted to select ‘Text’ tab and then check ‘Wrap Text’ in the Alignment section

Table 5.3: Settings of St Andrews collection

Table 5.4: Miscellaneous Property Adjustments



After all these adjustments are done and the data set is filled, this entire set needs to be

published into the CXML file along with the set of Deep Zoom images. This is achieved by

using the “Publish” button (shown in Fig. 5.4).

5.5 Creating Pivot Silverlight Application

Now since we already have the CXML file and the associated Deep Zoom collection, this

has to be deployed using an ASP.NET server to create a web interface. To begin, a new

Silverlight Application needs to be created in Visual Studio 2010. After that, the

“System.Windows.Pivot.dll” needs to be referenced. This file can be located in the

directory where the PivotViewer SDK has been installed.

Fig 5.4: Image showing Pivot ‘Publish Button’

Fig 5.5: Creating Pivot Silverlight Application



Next, the file MainPage.xaml needs to be opened and updated with XMLNS declarations

to allow PivotViewer control namespace:

After this, we need to code in C#.NET to load the CXML collection into the ASP.NET

server. For this the file MainPage.xaml.cs (the code behind of MainPage.xaml, accessed

by double-clicking it) is to be modified so that it can match the CXML with a local port

and deploy. The code is given below:

<UserControl x:Class="SilverlightApplication1.MainPage" xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation" xmlns:x="http://schemas.microsoft.com/winfx/2006/xaml" xmlns:d="http://schemas.microsoft.com/expression/blend/2008" xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006" xmlns:pivot="clr-namespace:System.Windows.Pivot;assembly=System.Windows.Pivot" mc:Ignorable="d" d:DesignWidth="972" d:DesignHeight="298" Loaded="UserControl_Loaded"> <Grid x:Name="MainCanvas">  <pivot:PivotViewer Name="PivotViewerControl" Grid.Column="0" ItemDoubleClicked="PivotViewerControl_ItemDoubleClicked"> </pivot:PivotViewer> </Grid> </UserControl>

using System; using System.Collections.Generic; using System.Linq; using System.Net; using System.Windows; using System.Windows.Controls; using System.Windows.Documents; using System.Windows.Input; using System.Windows.Media; using System.Windows.Media.Animation; using System.Windows.Shapes; using System.Windows.Pivot; using System.Windows.Browser; namespace SilverlightApplication1 { public partial class MainPage : UserControl { private static readonly string CXMLCollectionUri = "http://localhost:42912/ClientBin/PivotCollection.cxml"; public MainPage() { InitializeComponent(); PivotViewerControl.LoadCollection(CXMLCollectionUri, ""); }

Fig 5.6: MainPage.xaml



Visual studio automatically creates a Web application that hosts the PivotViewer

Silverlight application when it is created – one is a dynamic ‘aspx’ file and the other is a

static ‘html’ file but both with similar codes.

Finally the html file has to be coded to allow binding of the CXML as follows:

private void OpenLink(string linkUri) { HtmlPage.Window.Navigate(new Uri(linkUri, UriKind.RelativeOrAbsolute), "PivotViewerSampleTargetFrame"); } private void PivotViewerControl_ItemDoubleClicked(object sender, ItemEventArgs e) { PivotItem piv_item = PivotViewerControl.GetItem(e.ItemId); if (!string.IsNullOrWhiteSpace(piv_item.Href)) { PivotViewerControl.CurrentItemId = e.ItemId; OpenLink(piv_item.Href); } else { MessageBox.Show("No Web Page..."); } } private void UserControl_Loaded(object sender, RoutedEventArgs e) { } } }

<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <html xmlns="http://www.w3.org/1999/xhtml" > <head> <title>SilverlightApplication1</title> <style type="text/css"> html, body { height: 100%; overflow: auto; } body { padding: 0; margin: 0; } #silverlightControlHost { height: 100%; text-align:center; }

Fig 5.6: MainPage.xaml.cs



</style> <script type="text/javascript" src="Silverlight.js"></script> <script type="text/javascript"> function onSilverlightError(sender, args) { var appSource = ""; if (sender != null && sender != 0) { appSource = sender.getHost().Source; } var errorType = args.ErrorType; var iErrorCode = args.ErrorCode; if (errorType == "ImageError" || errorType == "MediaError") { return; } var errMsg = "Unhandled Error in Silverlight Application " + appSource + "\n" ; errMsg += "Code: "+ iErrorCode + " \n"; errMsg += "Category: " + errorType + " \n"; errMsg += "Message: " + args.ErrorMessage + " \n"; if (errorType == "ParserError") { errMsg += "File: " + args.xamlFile + " \n"; errMsg += "Line: " + args.lineNumber + " \n"; errMsg += "Position: " + args.charPosition + " \n"; } else if (errorType == "RuntimeError") { if (args.lineNumber != 0) { errMsg += "Line: " + args.lineNumber + " \n"; errMsg += "Position: " + args.charPosition + " \n"; } errMsg += "MethodName: " + args.methodName + " \n"; } throw new Error(errMsg); } </script> </head> <body> <form id="form1" runat="server" style="height:100%"> <div id="silverlightControlHost"> <object data="data:application/x-silverlight-2," type="application/x-silverlight-2" width="100%" height="100%"> <param name="source" value="ClientBin/SilverlightApplication1.xap"/> <param name="onError" value="onSilverlightError" /> <param name="background" value="white" /> <param name="minRuntimeVersion" value="4.0.50401.0" /> <param name="autoUpgrade" value="true" /> <a href="http://go.microsoft.com/fwlink/?LinkID=149156&v=4.0.50401.0" style="text-decoration:none"> <img src="http://go.microsoft.com/fwlink/?LinkId=161376" alt="Get Microsoft Silverlight" style="border-style:none"/> </a> </object><iframe id="_sl_historyFrame" style="visibility:hidden;height:0px;width:0px;border:0px"></iframe></div> </form> </body> </html>

Fig 5.8: HTML coding to bind CXML



Finally, the Deep Zoom folder and the CXML file that has been published using Excel

Pivot tool should be added to the ClientBin folder located in the Silverlight Web

application directory of the application that has been created, as it contains an XAP file

which needs to be accessed by the program while building the solution. The solution is

then built and run, and we have the Pivot Collection of St. Andrews University historic

photographs.

Fig 5.9: Pivot collection of St. Andrews University photographs.

Fig 5.9: Facet categories in

Pivot collection of St.

Andrews University

photographs.



5.6 Design Checklist

Microsoft Silverlight team (2011) has laid down a design checklist for implementation of

an efficient PivotViewer interface. For the purpose of this experiment, the guidelines

provided by them have been closely observed while implementing the system:

[✓= Checked; X= Not Applicable; ? = is investigated]

Basics

Is the collection named well? Would the target user

understand what it is about? ✓

The collection is named St. Andrews University Photographic collection

which is easy to understand by users.

Good quality iconography, branding and attribution? x

Since this is not a commercial product, these attributes are not

considered given the scope of this piece of study.

Is the default view interesting given the subject matter and

target user? ?

The default view shows the entire collection of the photograph, which

is later evaluated from the user experiments.

Images

Images should be of good quality ✓

Only the images with higher resolution (i.e. those having an extension

“_big”) from the supplied images is chosen.

Possibility of adding text or graphics to the items? x

Given the variety of topics and categories, extra graphics would mean

too much noise in the collection. Hence this is not included. Besides,

the metadata already had sufficient texts and hence no extra text or

graphics has been included.

Facet

Categories

and

Facets

Careful attention to the categories in the filter pane?

Number of such categories? (5-8 is ideal) ✓

The categories are given according to the metadata provided, and there

are 6 filterable categories.

Should any be added? x

Given the current metadata associated, no extra needs to be added.

Are there any categories a user might need not in the

collection currently? ✓

Careful attention is taken to include only those as filterable

categories that the users might actually need.



Are there any categories with a long list of facets? If so, can

the values be collapsed? ✓

Care is taken to keep these categories in the info panel only.

Are the categories in the right order? ?/ ✓

The categories are ordered according to the metadata provided and

hence it is subjective and mainly depends on the overall view of what

the user actually thinks after using the interface.

Are the categories named properly? Do they use the

correct conventions and will your target user be familiar

with the terms used?

✓

Care is taken to include simple and effective conventions that user

would be familiar with.

Are the facet values formatted properly? ✓

The facets are formatted according to the content, e.g.- the year is

numerical with 0 decimal places etc.

Should this collection be linked to other collections? ?

This collection could be expanded to include the overall 300,000

images archived by St. Andrews University Library, assuming they

have similar faceted data associated, however this could be further

investigated in future studies.

Are you happy with the search results? ?

Since this is based on user experimentations it has been discussed

further in user evaluations

5.7 User Experiments

To carry out a holistic yet significantly deep evaluation of the PivotViewer as compared

to traditional information retrieval and visualisation systems, user-centred lab based

experimentation was set up.

Table 5.5: Design Checklist



5.7.1 Ethical Considerations

The user-evaluation was conducted according to the ethics guidelines laid down by the

University of Sheffield for post graduate students:

http://www.shef.ac.uk/is/research/ethics/pgt.html

The users were contacted via e-mail and social media sites along with an attached

Participant Information Sheet. Before the experiments were conducted, they were asked

to sign the Participant Consent Form [Appendix]. Every user was allocated a unique

identifier (S1, S2, ... , S11) which enabled in keeping the participant data collection

anonymous.

5.7.2 Selection of participants

Since the use of Pivot interface is aimed at general users with only basic PC experience,

there was no requirement for setting up a special user group with special qualities.

However, due to the time constraints and the nature of qualitative research required,

instead of a random set of people, a small number of volunteers from various age groups

and with varied education and information searching experience was chosen. These

volunteers comprised of a multi-cultural group of ten postgraduate taught students from

the Information School at the University of Sheffield.

5.7.3 Rationale

In order to properly reach a consensus about the visualisation power of PivotViewer as

compared to the traditional systems, a series of observations in various categories and

scenarios are necessary to identify a set of common patterns. This would lead to a

tentative hypothesis that can ultimately help the study to culminate in a probable theory

of suitability about this technology. Hence, in order to achieve the aims of this study

(Chapter 2 – Section 2.2), it is required to carry out an inductive research. The overall

theme of the experiments was based on the exploratory interviews (Oppenheim, 1992),

to gather an in-depth data of the users’ ideas about the traditional visualisation systems

as compared to PivotViewer in its various aspects. Besides, according to Fowler (1995),

questions having multiple choices should encompass a wide range of choices to enable

sufficient detail for differential analysis. Hence, while building the questionnaire, care

has been taken to keep sufficiently wide ranges depending on the nature of the

questions.

http://www.shef.ac.uk/is/research/ethics/pgt.html



Incorporating the above considerations, the experiment was categorised into six

sections:

Preliminary Questionnaire

Evaluation of user-centred expectations

Pivot awareness

A general explanation of using PivotViewer

User-centred interface evaluation

Post evaluation questionnaire

5.7.4 Preliminary Questionnaire

The primary aim of this questionnaire is to gather if there exist any chief dependencies

amongst the participants in relation to the experiments that were undertaken. This

questionnaire was separated into two sections:

Basic Details

This section addressed the following areas:

Age/Profession/Education background

Familiarity with PC/Browser and prior experience levels in carrying out photo

search

Frequency of Internet usage

Confidence levels in Web search

General Familiarity

Frequency and popular topics/categories while using Web based search

Most favourable text-based search interface and the corresponding level of

satisfaction

Most favourable image-based search interface and the corresponding level of

satisfaction

Number of pages generally browsed while before stopping

Nature of image search techniques employed

Familiarity of image search techniques

Analysis on the levels of difficulty for the search techniques encountered

Vagueness encountered in search habit



5.7.5 Evaluation of user-centred expectations

This section tries to evaluate the users perceptions about the current information

retrieval systems based on various criteria as mentioned below. It tries to build an

overall notion of how the users currently feel the traditional interfaces are and what kind

of changes they would possibly want the systems to be like, in the future. The purpose of

this section was to identify if the users are satisfied with the systems they are currently

using and map their requirements to see if it fits in well with what PivotViewer has to

offer.

The questionnaire in this section was designed to find if the users were:

Satisfied with the current image and information retrieval systems,

Satisfied with browsing individual pages for information searching

Also whether they would want all images and information to:

Be accessible in a single browser

Be available on the web to be ordered and colour coded according to particular

categories within a single browser or interface.

Show their associated information, details and various miscellaneous properties

at the time of viewing.

Link to other images based on similar associated information/categories

instantly

5.7.6 Pivot awareness

Given the fact that this product was a recent launch of Microsoft Corporation in the

Silverlight domain, it would be interesting to find how many users actually head about

PivotViewer before. At the same time, it would be interesting to know, out of those who

have heard of it, how many have actually used it and have been familiar about with it.

This is necessary because it would be worth a study to actually familiarise the

participants with the interface and then try and relate that experience with the

traditional systems that they have been using for a long time.

This section had two short questions:

If the users were familiar with the Pivot interface

Their level of familiarity with the interface (if they are familiar).



5.7.7. A general explanation of using PivotViewer

Every participant (regardless of their experiences with Pivot and Web search), was

familiarised with the interface of PivotViewer. They were demonstrated to perform the

following:

Zooming in and out of the interface using mouse wheel as well as the controls

provided for the same

How to use the text-based search facility to bring out the required image

How to view the info panel and all the different facets in it

Familiarised the users with the different kinds of information in the info panel

and enables them to distinguish between filterable keywords and static

information.

How to use facet categories to perform multiple filtering

How to use the sort option under every facet category according to the necessary

requirements to order the filter lists

How to use the mouse to filter elements that had numerical data

Such a demonstration is provided to allow the users to have a direct interaction instead

of the PivotViewer Interface so that they are confident enough to carry out the

experiments.

Fig 5.12: Various features in

Pivot interface



Fig- 2 My Dissertation Interest lies

in the area of Visualisation for


5.7.8. User-centred interface evaluation

This is the central assessment section of the entire project. This section is divided into

three scenarios, where in each scenario, two interfaces were evaluated, one of them

being a Pivot interface. Users were asked to carry out three tasks for each of the

scenarios. The tasks were carefully crafted in a way such that they were based on

Shneiderman’s conceptual framework for image retrieval (Rosenfeld & Morville, 2002;

Spencer, 2006).

1. General searching familiarity with the systems

2. Searching for a known item (and/or related information)

3. Exploratory searching ( searching based on some defined multiple

categories)

After carrying out tasks in each of the given scenarios, the users were asked to fill out a

questionnaire that had several parameters of comparative evaluation between the two

systems. (More details in Appendix – User-centred Interface Evaluation)

Scenario A – Interfaces with exactly similar datasets

The St. Andrews University Library already has an interface where it has archived its

historic photographic collection. [Which can be accessed at: http://special.st-

andrews.ac.uk/saspecial/index.php]. Borrowing as a subset of 10,000 images from the

collection and using the same metadata that the engine uses, a Pivot interface was

designed as explained earlier. Now, in this first scenario, these two systems were

evaluated based on a set of parameters explained in the next section. This would enable a

perfect evaluation of between two interfaces that had exactly the similar images and

associated data and hence provide with an interesting find on what the users feel about

the original system and the new (prototype) system. (Questionnaire: Scenario A)

Fig 5.13: St. Andrews Interface (left) and Pivot interface (right) with exactly same datasets

http://special.st-andrews.ac.uk/saspecial/index.php

http://special.st-andrews.ac.uk/saspecial/index.php



Scenario B – Open ended interface and Pivot interface on a particular subject

The next scenario was based on evaluating between widely used open ended image

search interface i.e. Google Images and a sample Pivot interface that had a repository of

endangered species. Tasks were set for the users to carry out in both the interfaces based

on the criteria mentioned in the previous section. After the tasks were done, the users

were asked to fill out a set of questionnaire that compared the two systems based on a

few parameters. [The pivot interface was last accessed on 15th August, 2011 at:

http://www.352media.com/silverlight/pivotviewer/demos/endangered/endangered-

species.aspx]. (More details in Appendix – User-centred Interface Evaluation

Questionnaire: Scenario B)

Scenario C – Interfaces dedicated to a similar domain

This last scenario of tasks involved the user to carry out search in some of the largest

movie databases in the market currently. IMDb has one of the largest and most popular

movie databases in the world and has an interface that is capable of searching movie

details along with all its related information ranging from cast to genre. Netflix also has a

similar repository but based on Pivot viewer. Similar to the above two scenarios, the

users were asked to perform a same set of tasks in both the interfaces and fill out the

comparative questionnaire to evaluate both the systems. (Questionnaire: Scenario C)

Fig 5.14: Endangered species Pivot interface (left) and Google Images (right)

Fig 5.15: IMDb (left) and Netflix PivotViewer (right)

http://www.352media.com/silverlight/pivotviewer/demos/endangered/endangered-species.aspx

http://www.352media.com/silverlight/pivotviewer/demos/endangered/endangered-species.aspx



5.7.9. Interface evaluation parameters

As mentioned in all the above three scenarios, the users were asked to take a

comparative evaluation questionnaire for each of the systems as soon as they finished

the tasks assigned for each of the above scenarios. For this a set of parameters were set

to obtain a comparative evaluation of the systems. The criteria/parameters for

evaluation were common for all the systems evaluated above. They are given below:

Evaluation Parameters

Procedure

Time taken (sec) The total time taken for the individual tasks was

monitored for each of the above interfaces, in every

individual scenario, using a stopwatch.

Number of clicks

The number of clicks to carry out each of the tasks was

monitored using an application that was set to register the

number of mouse clicks.

Satisfaction

The users were asked specifically give their ratings for the

individual interfaces how satisfied they were and

interactive they felt while during carrying out the tasks.

Usability

The users were asked to provide their views on how

usable they felt about each of the interfaces and how easy

they found the interfaces.

Visual Appeal The users were asked to rate the overall visual appeal of

the interfaces in relation with the image retrieval process.

Confidence Having used the interfaces to carry out the tasks, the users

were asked to rate their confidence in using the interfaces.

Results

Finally, the users were asked to rate how they felt about

the results retrieved by the interfaces for the tasks that

were carried out and also how they felt they have done in

the tasks.

(*More details in Appendix: – User-centred Interface Evaluation Questionnaire)

Table 5.6: Parameters for interface evaluation*



5.7.10. Post evaluation questionnaire

This was the final section of questionnaire and was divided into two parts. This set of

questionnaire was mainly aimed at getting a proper structured feedback based on the

overall task-based experimentations carried out. It was divided into two sections, part

one being a point based questionnaire to gather views of the users on a scale of 1 to 5 to

indicate the agreement or disagreement of the user in connection to the questions asked.

The next section was more open and encouraged open answers. The two sections are

briefly discussed below:

Part 1

This section consisted of three categories on which the users’ views were gathered:

Tasks taken

The views of the users were taken to indicate if they felt that the

tasks undertaken were realistic and whether they had a clear idea

on what they were supposed to do. It was also asked if it was a

new experience for them in working with picture collections in

terms of the visualisation provided by PivotViewer.

(more details in Appendix – Post Evaluation Questionnaire)

Pivot interface

The following areas of the pivot were addressed in this category to

get the users views:

The clarity of the facets and descriptions in Pivot interface.

Whether the facets and categories helped them to have a

mental picture of what they were searching for from the

whole collection.

If the Pivot was helpful due to its categorical arrangement

and whether it increased their interest in using it further.

If the pivot interface was difficult to use.

(more details in Appendix – Post Evaluation Questionnaire)

Results

retrieved by

Pivot

Here a detailed analysis and evaluation on the retrieved results

from the Pivot was obtained through a detailed questionnaire.

Users were asked if the images were hard to comprehend and

whether the details provided by pivot was enough. The

arrangement and overall functionality of the Pivot interface

compared to traditional page-wise browsing was also attempted

to be covered in this section. (Appendix: Post Evaluation Questionnaire)

Table 5.7: Post evaluation questionnaire categories Part- 1



Part 2

This is the penultimate part of the experimentation where the users are finally asked to

indicate their preferences between Microsoft PivotViewer and traditional retrieval

systems. This was followed by three open ended questions that required the participants

to put their views regarding:

The advantages of the interface they preferred that made searching easier for

them

The disadvantages (if any) that might have made searching difficult for them

Any extra features or functionalities that the users wish to be included in future

for their preferred choice of interface

5.8 Pilot Study

A pilot study was undertaken with the help of a lecturer in Information Systems at the

University of Sheffield as a participant, prior to taking the actual experiments with the

users. The fundamental reasons for carrying out this study were to:

Develop and test the adequacy of the research/experimentation

Assess the feasibility of the research

Analyse if the research is realistic and workable

Identify issues which might occur using the proposed methods

Collect a preliminary data

Assess data analysis to uncover potential issues

Determining the approximate time required to complete the experimentation

Having taken the pilot studies, a few changes were introduced in the methodology

adopted to carry out the experiments.

Firstly, it was noted that the interfaces took significantly long time to load from the local

system every time during the experimentation. As such, it was recommended to run the

applications before the beginning of the experiments and make full use of the local cache.

This would reduce the time taken for users to wait during the experiments and avoid

unnecessary delay.

Secondly, it was identified that the users also needed time to actually familiarise

themselves with the PivotViewer interface. As such, an extra session was allocated to

allow the users to play around with the interface in general. At the same time, sessions

were also included for the participants to carry out general searches on each interface,

before the beginning of all the scenarios during the experiment, so that a state of

saturation is reached on their level of confidence in using the interfaces so that a proper

and fair result is reached.



Given the time constraint, the post evaluation questionnaire was condensed so that users

can provide a much deeper insight. At the same time, certain ambiguities in the

questionnaire were eradicated to make the whole experiment more user-friendly,

interesting and enjoyable.

5.9 Timetable

From the pilot studies and self evaluations, the total approximate time for the whole

experiment has been tabulated below:

Stages of Experimentations

Expected Time (in minutes)

Minimum Maximum

General Tutorial/Awareness of Pivot 5 7

Allowing the user to familiarise with the Pivot interface 5 7

I - Preliminary Questionnaire – Basic Details 1 2

II - Preliminary Questionnaire – General Familiarity 3 5

III - Evaluation of User-centred expectations 1 2

IV – Pivot Awareness

V – User-centred Interface Evaluation

Scenario – A Tasks + Evaluation 10 12

Scenario – B Tasks + Evaluation 12 15

Scenario – C Tasks + Evaluation 12 15

VI - Post evaluation questionnaire - Part 1 2 3

Post evaluation questionnaire - Part 2 3 5

Total expected time for evaluation completion 54 71

Table 5.8: Approximate time needed for total experiment



5.10 Summary

In this chapter, the research methodology taken to carry out this study has been

explained with a brief overview highlighting the literature adopted for this purpose. The

dataset used from St. Andrews University Library photographic collection has been

explained with an overview on the metadata. This was followed by explaining the actual

steps in building the Pivot collection and finally creating the application in Visual Studio

has been explained. For the purpose of an effective system, a design checklist has also

been followed which was recommended by Microsoft. Next, the user experiments taken

were explained stepwise, having briefly illustration the ethical considerations, selection

of participants and the rationale. Next, preliminary questionnaires were discussed

followed by the evaluations of user-centred expectations that were carried out. A short

study taken to find the Pivot awareness was also indicated before explaining the user

centred interface evaluations that were carried out. A brief introduction about the

PivotViewer interface that was enlightened to the users has also been highlighted. The

parameters set for the evaluation of the Pivot were also discussed along with the reasons

behind post evaluation questionnaire. Finally, it was concluded with the discussions

about the pilot study undertaken, along with a short timetable indicating the overall time

required for the whole study.



Chapter 6

The methodologies and ideas previously discussed in Chapter 4 were implemented and

experiments were successfully carried out with the help of 11 participants that included

the single pilot study. Ideally, the data gathered from a pilot study should be ignored,

however, in this case it is included since it had no major effect on the data, but only

identification of issues related to time adjustments in the experimental setup, the

ambiguity of a few questionnaire and the manner of tests conducted, which was rectified,

as mentioned in the previous chapter. The interfaces was designed and chosen carefully.

The tasks were set in such a way so that users could carry out the tasks effectively and at

the same time exploit and stretch the limitations of the systems as much as possible.

This chapter will present the results for the study undertaken through the detailed tasks

and user evaluations. A set of detailed data and graphical results are presented for each

of the experiment undertaken on the three different interfaces as well as the users’ views

and recommendations. At the end of each sub stage of experiments taken, a summary of

results are also discussed.

6.1 General User Overview

The participants who agreed to volunteer for the experimentation were all currently post

graduate taught students in the Information School at the University of Sheffield. While

most of them were only studying (64%), some were also working (36%) along with their

studies. Around 54.55% of the volunteers were in the age group between 26 to 33, with

the next highest being in the range 18 to 25 (36.36%).

RESULTS AND ANALYSIS

Fig 6.2 – Professional background of participants Fig 6.1 – Participant age group



A very high number of participants indicated that they had a proper IT educational

background (91.91%) along with proper PC skills (81.81%) with mean experience level

of 5.81 out of 6. As such, this experiment can be said to have negligible limitations

imposed by users being weak in

PC/IT skills, which would

otherwise have introduce some

constraints in the data

gathered. On the other hand,

this promises to make it more

challenging and interesting to

note, how they would perform

with the system, in an

unfamiliar and how well each

individual critique the system,

which we shall soon find out.

Around 54.54% users indicated that they had very good prior experience in photo

searching with an overall mean experience of 5.81 denoting that most users were

confident and aware of photo searching. The other users were not so sure, however, the

trend showed that they were quite aware and familiar with the concept.

Table 6.2 –User professions

Fig 6.3 – Educational background of participants

Fig 6.4 – User PC familiarity

Fig 6.5 – User photo search experience

Table 6.5 – User photo search

experience

Table 6.4 –Familiarity

with PC

Table 6.1 – Participant age

groups Table 6.3 – Educational background

of participants



There was no surprise in finding that today

Internet has become an essential part of our lives

with over 81.81% users indicating of using the

Internet several times during the day and the rest

using it at least once every day. However, when it

came to the level of confidence in searching the

views were not comparatively as high. Though

none of the users believed that they had a low

confidence level, around 34.37% of them still felt

that they were not very confident in using the

Web for searching.

6.2 Search Behaviour Patterns

While it was evident that users believed that they were quite familiar with searching and

search techniques, it was now necessary to take a step further and find out about their

search habits in a more detailed manner and establish some patterns for further analysis.

This piece of result is essential as it would throw light on the current trend in search

behaviour, the users likes and dislikes about the current interfaces, their tolerance and

satisfaction level with the present systems, certain insights on their searching strategies

and the difficulties that they might find in using these present systems.

Fig 6.6 – User Internet usage

Fig 6.7 – Confidence levels in Web searching

Table 6.7 – User confidence levels in

Web searching

Table 6.6 – User Internet usage



To begin with, the participants were asked to indicate their habit of using the Web for

searching. It was learnt that a little more than half of the participants (54.55%) search

for information on the Web several times during the day, while others (36.36%)

searched at least once every day and rarely (9.09%) certain times over a week.

Next it was essential to know the most popular search topic or category they generally

seek information on. This was essential since organisations looking for adopting or

implementing a suitable next generation experience in search interface in the future, can

benefit from the user trends and interests to begin with, and tap the scenario of demand.

The following results were obtained from the users, which showed a wide variety:

User Favourable search topic S1 ----No particular topic---- S2 News, Shopping, Events S3 Technology, Music S4 Academic literature S5 Movie, Jobs S6 News S7 IT jobs in UK S8 ----No particular topic---- S9 Cars

S10 Technology, Education S11 Research Papers

When it came to deciding about the actual interfaces, Google emerged as the clear winner

as the users’ choice for the most favourable and popular search interface for text-based

searching in the Web, with all the participants favouring it. But given the total support

for Google as the most favourable interface for searching information, not all users were

Table 6.8 – Online search habits

Fig 6.8 – Online search habits

Table 6.9 – Responses to

favourable search topics



highly satisfied with it. Around 45.46% of the users felt highly satisfied, with the overall

mean comparatively high in 4.45 out of 5.

When it came to opinion about a

favourable image retrieval system,

Google images again emerged as the

clear winner, with only a few users

indicating also about Flickr and Bing.

However, the level of satisfaction

with image searching proved to be

lower than that of textual

information searching with mean

only a little above moderate at 3.63

The overall satisfaction with the

current search interfaces has seen a

similar response at a mean of 3.63.

Table 6.10 – User satisfaction levels

for text based searching

Fig 6.9 – User satisfaction levels on

text-based searching

Table 6.11 – User satisfaction levels

for image searching

Table 6.12 – Overall traditional

search interface satisfaction levels

hing

Fig 6.11 – Overall traditional search interface satisfaction levels

Fig 6.10 – User satisfaction levels in image searching



Perhaps one of the most important findings was to get an idea of what the tolerance

levels of the users were in finding suitable information while searching page. For this

they were asked to indicate the number of pages viewed before they stopped while

searching for information. The results indicated a trend that users rarely searched more

than 3-4 pages, with the mean at 3.273. This is however subjective depending on the

search experience of individual people, but at the same time, it indicates a level of

persistence that users would likely agree to undertake in case there was a requirement.

The users were also asked to indicate their favourable image search techniques and

related familiarity to gather an idea about the strategies employed while carrying out

such tasks, so that it can be related to this study later on. The following responses were

received:

Favourable image search techniques f %

a. Enter the desired query in text-based format in search engines and obtain the images from first page usually with no further browsing.

9 81.82

b. Follow hyperlinks and navigate page to page until desired image is retrieved 5 45.45

c. Use specific image retrieval web-site(s) (e.g. – Flickr/Picasa) 4 36.36

d. Use content based search engine(s) 1 9.091

e. Other procedures 0 0

Fig 6.12 – Number of pages viewed by users, before they

usually stop searching for information

Table 6.13 – Number of pages

viewed by users, before they

usually stop searching for

information

Table 6.14 – Table indicating the favourable image search techniques and their

corresponding frequency/percentage of support by users



Experiences with image search techniques f %

a. Can search for a particular and unique image according to

need (e.g. – a well known architecture, famous painting etc.) 9 81.82

b. Can search for image(s) based on a broader category or

subject (e.g. - a family of endangered species, flowers etc.) 9 81.82

c. Can search for images on a specific subject (e.g. – a latest

game wall paper, a movie etc.) 9 81.82

d. Can search images with a combined multiple criteria, (e.g. –

movie wallpaper with a particular actor, within a genre and

the year of its release etc.) and have been successful in doing

so in the past.

6 54.55

Table 6.15 – Table indicating the array of experiences with image searching techniques

and their corresponding frequency/percentage of support by users

Fig 6.13 – Graph indicating the favourable

image search techniques (for each questions a

to e) and their corresponding

frequency/percentage of support.

Fig 6.14 – Graph indicating the array of

experiences with image searching techniques

(for each questions a to d) and their

corresponding frequency/percentage of support.



Users were also asked which of the procedure(s) they usually found difficult, from their

searching experiences in the past.

User Users’ views on difficult experiences in searching

S1 “Combined multiple criteria. Difficult to start”

S2 “Searching images on a specific subject”

S3 “Based on a broader category or subject needs to search several times”

S4 “Combined multiple criteria”

S5 --- NA---

S6 --- NA---

S7 --- NA---

S8 “Combined multiple criteria, because I cannot realise the information of the particular image”

S9 “The first procedure because it involves searching for unique things which may be unavailable”

S10 “Search for combined multiple criteria, hard to get the exact result, consistency”

S11 “The more general(2) and (4), where I’m exploring/learning what I am looking for”

From the above results a few patterns of emerge with respect to the difficulties faced by

users in their search methods. Users mostly have a lack of confidence in carrying out

searches that have multiple criteria or which may require crafting complex queries.

Besides, they are also issues related to consistency in the results obtained, depending on

the queries formulated in traditional systems to obtain such search results.

Finally, to address the issue of vagueness (Rodden, 2002), often encountered by users

while searching, it was asked if the users encountered situations where they accidentally

came across an information or image that was quite useful to them and whether they

would find it more favourable if the system that they usually search information from,

can address their cognitive sense to guide them to their desired requirements. Most of

the study participants agreed that they did come across situations, where they did not

know where to start from while searching for particular information. Almost all of them

however agreed that it would be useful if the system could assist in their level of

cognizance while searching for information in situations where the search criteria is not

clear.



6.3 Evaluation of user-centred expectations

As discussed in the previous chapter, a set of ratings were gathered from the users,

regarding how they felt about the key aspects and features of the information retrieval

systems that are popular today, and how they would perceive if a few changes were

introduced or added. The following table shows the results with the mean levels of

agreement on a scale of 1 to 5:

Scenarios Mean 1. Satisfaction level with current image and information retrieval interfaces most

familiar with.

3.7272727

2. Satisfaction level with the current system of clicking and browsing to retrieve

images and required information from individual pages at a time.

3.0909091

3. Perception levels if all images and information were accessible in a single

browser without browsing further.

3.4545455

4. Perception levels if all images and information available on the web or the

company could be categorised and colour coded according to particular categories within a single browser or interface.

3.6363636

5. Perception levels if all images and information had embedded visual

information (e.g.- short title or data on the image itself as a header).

3.7272727

6. Perception levels if all images and information could show their associated

further information, details and various miscellaneous properties when users are viewing them.

3.6363636

7. Perception levels if all images and information could link to other images based

on similar associated information.

3.7272727

Table 6.16 – Table indicating the users’ expectation levels on certain features in user

interfaces for information visualisation and retrieval

Fig 6.15 -



6.4 Microsoft PivotViewer awareness

Since Microsoft PivotViewer is a

comparatively new technology with no

notable or wide implementations yet, a short

section of the experiment was also taken in

order to know, if the users were aware of this

new technology, and how familiar they were

with using it. The results indicated that around

63.6% of the users were aware of this product,

or at least, heard or read about it. However,

there was no surprise to learn that around

81.82% of them have never used it before.

This probably made this project even more

interesting, since the users were introduced to

a new technology and given complete control

of it. This also ensured that the users could

provide their ideas without any pre-conceived

notions about the interface.

Table 6.17 –Familiarity with using

Microsoft PivotViewer

Fig 6.16

Fig 6.17 –Familiarity of using Microsoft PivotViewer



6.5 User-centred interface evaluations

6.5.1 Scenario – A

In this scenario, a similar set of tasks were carried out on St. Andrews University Library

Photographic collection and the PivotViewer interface designed for this study and the

time taken for carrying out each of the tasks and the number of clicks were recorded, as

shown in the table below.

Based on the above data collected, comparative charts were constructed to generate the

overall trend as shown below.

Table 6.18: Time taken for carrying out each of the tasks and the number of clicks- Scenario-A

Fig 6.18: Graphs comparing the time taken and number of clicks for completing scenario-

A- Task1 and 2



From the above charts it is clear that the time taken by users to carry out the tasks in

PivotViewer was much lesser than that in the original St. Andrews University Library.

The total time taken to complete the known task was 1.54 times faster and the complex

search was around 2.46 times faster in PivotViewer, as compared to the original

interface.

The number of clicks, on the other hand, for finding the known item was 1.27 times

higher in PivotViewer. But on the other hand, for carrying out a complex search, the

number of clicks was comparatively similar (around 1.05 times less) in Pivot interface.

Overall, the time taken by users to carry out the tasks in PivotViewer was 35.1279

seconds lesser or 1.925 times (almost twice) faster than the original interface. However,

on average, the total number of clicks on both the interfaces was similar with Pivot being

0.54 clicks more.

The interfaces were also evaluated on the basis of five performance criteria as shown:

Table 6.19: Performance results based on five performance criteria for St. Andrews interface

Table 6.20: Performance results based on five performance criteria for PivotViewer interface



The results indicated that PivotViewer also scored high in the performance criteria, with

overall mean of 4.25 as compared to that of St. Andrews Library interface at 2.65, i.e.

nearly 1.6 times higher than the original or existing interface. The highest margin of

difference was in the PivotViewer’s visual appeal which scored comparatively high than

the St. Andrews Interface. However, the only criteria where the two interfaces had the

smallest difference were in the confidence level of the users.

6.5.2 Scenario – B

In this scenario, a similar set of tasks were carried out on Google Images and a sample

online Pivot interface on endangered species. The time taken for carrying out each of the

tasks and the number of clicks were recorded, as shown in the table below.

Table 6.22: Time taken to carry out each of the tasks and the number of clicks-

Scenario-B

Fig 6.19: Interface evaluation summary- scenario-A

Table 6.21: Mean rating summary for

scenario-A



Based on the above data collected, comparative charts were constructed to obtain the

overall trend as shown below:

From the above charts it is clear that, the time taken by users to carry out the tasks in

PivotViewer was comparatively lesser than that in Google Images. The total time taken to

complete the known task was twice as fast and the complex search was around 2.13

times faster in PivotViewer as compared to Google Images.

The number of clicks, for finding the information related to the known item in

PivotViewer was lesser (1.42 times) than that in Google Images. However, for carrying

out the complex search, the number of clicks was quite higher (2.1 times) in Pivot

interface as compared to Google Images.

On an average, it was identified that the time taken by Pivot interface was much lesser

than that of Google Images. Overall, the time taken by users to carry out the tasks in

PivotViewer was around 41.6 seconds lesser and around 1.938 times (almost twice)

faster than the time they found similar required information using Google Images.

However, the on average, the total number of clicks on the Pivot interface was slightly

higher (1.81) than that of the traditional search interface provided by Google.

Fig 6.20: Graphs comparing the time taken and number of clicks for completing scenario-

B- Task1 and 2




According to the five performance criteria evaluated, the overall performance of Google

Images was moderate with the lowest being in the area of satisfaction with the interface.

But the users were quite confident, on the other hand, to use both the interfaces. Overall,

the Pivot interface performed better in all the criteria than the Google Images, in carrying

out the tasks given, with the overall satisfaction levels above 4.09 in all the sections, as

compared to Google Images, which performed comparatively less. This can also be

clearly noted from the chart above.

Table 6.23: Results based on five performance criteria for Google Images

Table 6.24: Results based on five

performance criteria for PivotViewer

Table 6.25: Mean rating summary

for scenario-B

Fig 6.21: Interface evaluation summary- scenario-B



6.5.3 Scenario – C

In this final scenario, a similar to the above to scenarios, a set of tasks were carried out

on two interfaces, the IMDb database and a Pivot interface created by Netflix. The time

taken for carrying out each of the tasks and the number of clicks were recorded, as

shown in the table below:

Based on the above data collected, comparative charts were constructed to obtain the

overall trend as shown below:

Table 6.26: Time taken for carrying out each of the tasks and the number of clicks- Scenario-C

Fig 6.22: Graphs comparing the time taken and number and number clicks

for completing scenario-C- Task1 and 2



From the above data it can be noted that the average time taken to search a known item

in IMDb was around 16.32 seconds slower than Netflix Pivot, which in other words was

2.29 times faster to retrieve. However, the average was slightly narrower during a

complex search, with the time difference of around 11.29 seconds by which Pivot

performed (1.52 times) better than IMDb. As for the number of clicks, both for the

known item as well as the complex search, Pivot had extra clicks of 1 and 1.54 clicks on

average respectively.

Overall, the Netflix PivotViewer interface enabled the users to carry out the tasks 27.61

seconds earlier (or 1.8 times faster), than the system provided by IMDb. However in

terms of the number of clicks, Pivot interface required relatively more clicks (an overall

average of 2.54) to accomplish the same tasks.


The Pivot interface continued to show overall high performance criteria, of average

rating above 4.54 in all the five sections. On the other hand, IMDb only had a maximum

performance level in the area of users’ confidence in using the interface and the

satisfaction levels provided in the results retrieved at an average of 3.45 and 3.40

respectively. Pivot interface, on the other hand, performed highly in all the areas,

especially in results retrieved, visual appeal and level of satisfaction.

Table 6.27: Results based on five performance criteria for IMDb

Table 6.28: Results based on five performance criteria for Netflix Pivot



6.6 Summary of interface evaluations

From all the above experiments, an overall summary was computed. The net average

time taken to complete all the tasks in traditional query based interfaces were found to

have taken 104.35 seconds more than that of Pivot, i.e. in Pivot, users carried out the

tasks 1.93 times quicker than traditional interfaces. At the same time, it was also noted

that the difference in time while performing complex search was much more, between

the Pivot and traditional technologies. However, the number of clicks required to

perform the tasks were found to be quite higher (average of 4.909 clicks more) than the

other interfaces.

These findings have been tabulated in the table below for each individual task

undertaken by the participants:

In order to have a further visual insight, the summary has also been depicted in graphical

manner, for each section of task scenarios.

Table 6.29: Interface evaluation

summary- scenario-C

Fig 6.23: Mean rating summary for Scenario-C

Table 6.30: Summary of individual task results between PivotViewer and other

(traditional) interfaces



In terms of performance criteria, PivotViewer has scored highly in all the scenario tasks

set for the users as compared to the other interfaces. While other interfaces had the

highest rating in the area of confidence, PivotViewer scored the maximum in the section

of ‘results retrieved’. This was followed by visual appeal, satisfaction, usability and finally

level of confidence. If we consider only the other interfaces, we can notice that the level

of confidence was the only section they rated highly compared to all other features. This

indicates that they mostly tend to use the interfaces since they may be quite popular

interfaces and they are confident in using them. However, in terms of Pivot interface,

confidence levels, though higher compared to other interfaces, apparently scored lower

than all other aspects indicating, that

users identified themselves more with

an experience that they liked, in a

visually appealing interface in Pivot

that is satisfying, useable and

retrieved relevant information

quickly.

Fig 6.24: Summary of time taken (left) and no of clicks (right) for all the tasks

Fig 6.25: Overall interface evaluation summary Table 6.31: Overall performance summary



6.7. Post Experimentation Results - 1

6.7.1 Evaluation of Tasks

Three key areas were focussed in relation to the experiments taken by the users, and

their views were gathered in these aspects. The participants were asked if the

experiments were realistic, to which the

mean satisfaction level was 4 out of 5,

which meant that users did feel that the

experiments undertaken were realistic. At

the same time, they also seemed to have a

clear idea (4.36) of what they were

supposed to do. One important aspect was

in finding if the users had a new

experience through this experiment, to

which they did respond well, with a high

rating level of 4.36, to which the users felt

that this was a novel experience in

information retrieval through

visualisation.

6.7.2 Results on Pivot Interface

A few general questions regarding Pivot interface was asked and views were obtained

from the participants in terms of how strongly they felt about this new interface, based

on four criteria –

Providing a clarity of description and facets

Helping in forming a ‘mental picture’ of the information to look for

Helping in retaining interest

Easy/difficulty in using

The highest agreeable result in the above categories was that, the Pivot interface helped

in retaining the interests of the users (4.63). The second highest category was that the

PivotViewer helped the users in forming a ‘mental picture’ (4.18) of the information they

were looking for by sublimating the area of vagueness that users often tend to be in

when searching for information (Rodden, 2002; Marchioninni 1992). The users also felt

Fig 6.26: Analysis on tasks undertaken

Table 6.32: Results of

tasks undertaken



that the descriptions in the interface and the facets were clear and helped them in finding

the required information. But it was also interesting to know, that though this was a new

interface to many (81% having never used it before), they did not find it difficult to use.

The reports are shown in table and graphical form below:

6.7.3 Technique and Quality of Results Retrieved by PivotViewer

To study the quality of the results retrieved and the technique incorporated in

PivotViewer, the analysis was based on a set of four areas, the mean agreement results

for each of which is provided in the table shown below:

Table 6.33: Results of Pivot interface

Table 6.34: Technique and Quality of results retrieved

Fig 6.27: Analysis of Pivot interface



From the above readings, it can be said that though there were thousands of images

accommodated in a single interface, unlike traditional Web interfaces, the images

retrieved by PivotViewer was not hard to comprehend. Also in addition, it can be said

that the overall collection did not overwhelm the users. The details associated with the

images, though included only as a panel on the right, the users felt that it was quite

enough in terms of its content and the information they were looking. However, they did

agree mostly to the proposition, that traditional browsing interfaces could accommodate

more details than the Pivot interfaces. In terms of the technicalities in Pivot, the users

majorly found the interface very easy to zoom in and out. They also strongly felt, that the

arrangement of the pictures and/or information, according to the various categories, was

quite useful and that they were able to find out the information they were looking for

relatively quickly.

Fig 6.28: Analysis of technique/quality of results retrieved by PivotViewer



6.8. Post Experimentation Results - 2

From the final section of the experimentation, views were gathered regarding the

advantages and disadvantages of the PivotViewer as a search interface for retrieving

visual information from a large data collection. An open-ended methodology was

adopted as explained earlier, for which the users indicated the following views regarding

the advantages of the PivotViewer as compared to the traditional interfaces:

User Advantages of Pivot interface that made searching easier as compared to

traditional interfaces

S1 “• Easy to use • Simple • Clear & objective • Efficient”

S2 “• Easier to search • Less time-consuming • Specific and clear”

S3 “It is very clear and easy to find the information that I am looking for, also I am very used to using it”

S4 “•Option for distinctive search •Easy to use”

S5 “More user friendly and targeted”

S6 “The interface of Microsoft Pivot is so user friendly”

S7 “• The visual display of search results • The interface”

S8 “The filtering functions and window in the middle”

S9 “The categories and interface of the system”

S10 “Everything is there, easy, availability, a combination of images and info”

S11 “•Consistent category of information • Easy to slice and dice the collection”

Clearly from the above data and user’s opinions, the following key advantages of using

PivotViewer can be proposed:

Easy to use: This is perhaps one of the prime advantages of PivotViewer. Almost every

participant indicated that the Pivot was easier to use than the traditional interfaces.

Searching was easier and the users felt more comfortable and interested. The interface

was simple and user friendly.

Visually appealing: Powered by Windows aero technology, Pivot was visually appealing

to the users, in terms of retrieving the images or information, grouping them into

categories, slicing and dicing them according to the filter criteria, and displaying the

results along with its associated metadata. Besides, the layout was also quite appealing.

Quick: The overall time taken by the users practically was faster than the traditional

interfaces. This can also be attributed to the friendly and easy to use interface design.

Consistent search results: It provided consistent and clear results for the information

required.

Table 6.35 – User views on PivotViewer advantages



The users were also asked to indicate some of the disadvantages of Pivot if any, and the

following few responses were received:

User Disadvantages of Pivot interface as compared to traditional

interfaces Relevance

S1 ---- none----

S2 ---- none----

S3 “There always come out same pictures which is not useful” ✓

S4 “Fixed options seems to prohibit flexibility of search” ✓

S5 “No”

S6 “No”

S7 ---- none----

S8 “No”

S9 “The overall presentation of the view not attractive and a bit blurry” ✓

S10 “Maybe when first time use, I feel unconfident, but later, I really like it when I get familiar with the interface”

✓

S11 “Switching between it and web pages may be awkward” ✓

From the above data, user’s opinions during the experimentations, and self evaluations,

the following disadvantages can be proposed:

Rigidity: The PivotViewer is quite rigid in terms of what it presents to the users. It

generally consists of a set of categories, based on a particular domain of implementation.

As such it has little flexibility in terms of incorporating additional categories without re-

generating the entire collection. Also sometimes users may want to see all the available

pictures or information, rather than just the information provided in the data collection.

Application load time: The initial load time of the application is another disadvantage of

the interface. Though being an application, since it is Web based, this cannot be ignored

since users tend to switch between websites or may navigate away from the page by

following an URL associated with the image or information. For such open ended-

navigation, every time the user comes back to the URL with the Pivot interface, the CXML

will take time to be loaded into to the Silverlight platform. As such, overall searching

flexibility could be narrowed and slowed down. The larger the collection of information

or images to be published, the slower is the application load time.

Build Time: The initial building time for the Pivot collection is quite delaying and slow

and the time required to build is directly proportional to the size of dataset.

Table 6.36 – User views on PivotViewer’s disadvantages



Non-sharable content: The information from a PivotViewer can only be retrieved or

noted down, but has no features to allow downloading or sharing of the contents.

Non-portable: The facets and facet categories are decided in advance, meaning that it

cannot be ported easily for different categories of items and has to be re-designed.

The users were finally asked to provide recommendations for additional functionalities

that they would like the interface to have in future and the following recommendations

were received:

User Extra functionalities and features recommended to be included Relevance

S1 “Notes, share, recommendations; Users can supply images” ✓

S2 “Voice over search” ✓

S3 “The page can change by itself then I do not need to click ‘next’”

S4 “Recommendations on search related items” ✓

S5 “I think it is a new concept for me and it was quite useful”

S6 “No”

S7 “Edit or add metadata” ✓

S8 ---- none----

S9 “Cool design and layout”

S10 ---- none----

S11 “Ad-hoc categories” ✓

User Other Comments Relevance

S1 “You can search if you do not know what you are looking for” ✓

S2 “Great job”

S3 ---- none----

S4 “Overall much better way of retrieving images and information than other applications already available and in use”

✓

S5 ---- none----

S6 ---- none----

S7 “Good experience” ✓

S8 ---- none----

S9 “The Pivot is a nice technology and will prove to be good in the years to come”

✓

S10 ---- none----

S11 ---- none----

Table 6.37 – Extra functionalities and recommendations for interfaces

Table 6.38 – Additional comments



6.9. Summary

This chapter began with a report on the general overview of the users which participated

in the study. A majority of the study participants were IT literature which indirectly

helped in the proper evaluation of the interface. Though many of the users indicated of

having high experience in photo and information searching, only 63% indicated of

having a high confidence in searching, the major factor in it being in the difficulties

encountered in complex searching. There was a high level of satisfaction in text-based

satisfaction in Google, however in the end of the study, PivotViewer scored higher in

terms of satisfaction. Apart from this, several other factors were also evaluated in

relation to the PivotViewer as compared to the other interfaces, where in each criteria

the PivotViewer managed to score higher than the traditional interfaces. However, the

number of clicks used in PivotViewer was more as compared to the traditional interfaces

evaluated. This was followed by the report on post evaluation results that indicated a

clear, fair and realistic experimentation. The technique used by Pivot and the quality of

the results retrieved by pivot was also strongly appreciated by the users. Finally, based

on the overall results and the post evaluation questionnaires, a few advantages and

disadvantages of the Pivot system were highlighted. Results were reported for each

individual tests undertaken and was appropriately illustrated in graphical

representations throughout the results.



Chapter 7

This chapter aims to discuss the results that have been obtained from the

experimentation. It should be noted that though the sample size of the participating

users were only a few, the study process was crafted in such a manner that the users

were intrigued to think about every area of the experience they might have encountered,

and hence provide with quality response. The overall data from the obtained results have

indicated some clear patterns which are discussed here.

Microsoft PivotViewer is a technology that incorporates and deals with the

representation, storage, organisation or and access to information items, or in other

words, it matches the definition of a proper information retrieval system, as suggested

by Baeza-Yates and Ribeiro-Neto (1999). At the same, PivotViewer is an interactive

system that helps in visually representing data to amplify cognition, something that Card

et al. (1999) considers a proper information visualisation system. As a result, from the

results obtained and according to the views of the researchers, PivotViewer may be

considered as a visually interactive information retrieval system. It can also be

considered to be a custom developed system to digitise collections, as studied earlier by

Hoeber (2007) and Rodden (2002), considering its main property to digitise collections.

From the results obtained, as well as user comments received, Pivot has been identified

to provide the data quickly (Table 6.30) and consistently across data collections. It

minimises the possibility of retrieving non-relevant documents, something that

Rijsbergen (1979) indicated, should be a significant feature of any proper information

retrieval system. However this entirely depends on the semantics of the metadata

included in the data collection. While some authors have mostly concentrated on images

(Baeza-Yates and Ribeiro-Neto, 1999) many have researched on text and documents

(Voorhees, 2004). PivotViewer provides the means to bring these two categories

together. Though it was not concentrated in this project, textual data can be represented

in the same way as image data by using appropriate faceted links to documents.

[NB: Tables and figure names are often included in the discussions in relation to Chapter 6 - Results]

DISCUSSIONS



Today, users have a high familiarity with computers (Table 6.4) and are known to

develop an overview of relations and patterns from the information they seek, through

the abstract data (Card et al, 1999). Given the level of interactivity offered by

PivotViewer, in allowing the users, to quickly and efficiently ‘slice and dice’ across

selections from an ever growing information space, it can be said that it helps in

increasing their cognitive vision and intelligence, as indicated by researchers in various

studies (Chen 2004, Nguyen and Huang, 2004, Spence, 2001; Card et al, 1999,

Shneiderman, 1996). This can be attributed to the fact, that users rated the PivotViewer

quite highly in several criteria (Table 6.33 and 6.44) in terms of the interface, the

visualisation techniques used and the quality of the results received. Addressing the area

of ‘cognitive ability’, users were particularly asked in the post-evaluation questionnaire,

if the interface helped in forming a ‘mental-picture’ to which users highly agreed with a

mean rating of around 4.18 (Table 6.28). The aspects of PivotViewer, as an Information

retrieval system and an information visualisation system is further assessed, based on

the previous research works, as well as the evaluations carried out in this study.

7.1 PivotViewer as an Information Retrieval System

Pivot thoroughly exploits the technique of ‘categorisation’ in information search, as

initiated by Smith et al. (2006), by introducing interactive exploration through facets. It

adds the richness in its interface, by enabling the users with the possibility to obtain

some really granular information that is both easy to comprehend (Table 6.34) and clear

in its descriptions, which helps in retaining the interests (Table 6.33). At the same time, it

should be mentioned here that Pivot furnishes the technology that facilitates the users to

see the larger trends in the data and gather a holistic view of the required information,

by combining it with the granulated detail.

In some collections, the value can also be in exploring and finding out something we have

never known before (Rodden, 2002). In other collections we can find some very specific

set of information, or if the information is seen as an aggregate, some insights may pop

out which one may haven’t seen otherwise. Perhaps one of the most intriguing features

of PivotViewer is its efficacy in arranging data collections, according to the desired

criteria chosen by the user and also allowing to selectively filter those collections and

gather subset of that information, smoothly and quickly. As discussed in Section 6.7.3

and shown in Fig. 6.29, users not only had a positive response in the Pivot’s ability to

quickly retrieve the information and also present a proper arrangement of the

collections, the overall time as noted down during the experimentations from Table 6.30

and Fig 6.25, also provides a further indication to this phenomenon.



Perhaps appropriate precision and recall metrics to order fetched results, as indicated by

Hoeber (2007), is used to order the results according to the information needs, keeping

in mind that the section in the info panel of Pivot also fetches related links from the Web,

which may be associated with the item being viewed. Pivot is not just about queries but

is also quite a dynamic process, which according to Kuhlthatu (1991), should be an

inherent property in information seeking, that involves the users ‘thoughts, feelings and

actions’. The results from the performance criteria, for each of the scenarios set up for

experimentations, indicated a positive result favouring PivotViewer in this aspect as

compared to the traditional systems. At the same time, by allowing users to follow their

own curiosity, Pivot effectively outlines the aspects of information foraging theory, as

described by Pirolli et al. (2001). However, Pivot has also maintained the traditional

querying technique in its interface, which according to Frunas (1997), is a good starting

point to ascertain a quality information scent.

It has often being indicated that users generally have a ‘low tolerance’ level (Spink et al.

2001), when searching for information. Also in this study, on an average, users are seen

to generally view approximately 3 pages, before they stop searching for any information

(Table 6.13, Fig 6.12). This is because users are found to mostly carry out searching only

on text or query based retrieval systems (Table 6.14 - Part a). Pivot on the other hand

introduces a multifaceted visual environment to search information, which is a

completely new experience (Table 6.32), something that most users are not familiar of

(Fig. 6.17). Provided the metadata associated with the collection are relevant, Pivot

isolates the possibility of generating instances of insignificant contents, which according

to Chau (2011), is on the rise today and often visible in the top 10 to 5 list. As traditional

information retrieval systems are said to be inefficient to handle it (Yang, 2005), Pivot,

on the other hand is a novel technology, and has a whole new dimension to unfold

through further commercial expansion into key businesses and research. Pivot also

successfully addresses the issue of dealing with short unfocussed queries, which is said

to be a challenge in traditional retrieval systems as expressed by Yang (2005).



7.2 PivotViewer as an Information Visualisation System

Users have increasingly shown a tendency to perceive information better and carry out

tasks more efficiently in PivotViewer as compared to the other traditional systems (Table

6.30, Table 6.31). This was also shown through various information visualisation studies

previously taken (Carter 1947; Pinker 1990; Hearst 1995; Xiang et al. 2005).

As discussed in Chgapter 2, Hoeber (2007) indicated that spatial coordinates, shape,

colour etc. are required to be carefully considered as an intrinsic requirement, while

designing a proper visually interactive interface, which can perhaps also influence the

human cognizance (Card et al., 1999), something which PivotViewer has incorporated

through its simplicity and structure. In fact, though it was an interface which most of the

users were not familiar with, their confidence levels in the tasks taken compared to the

other traditional interfaces, were higher (Fig 6.26, Table 6.31). Also from the

experimentations (particularly Scenario B) carried out in this study, it was seen that the

PivotViewer interface could make use of different colour representations, to allow the

users to easily scan across datasets and look for trends and interesting patterns. In other

words, it has been designed in such a way, so that it allows the users a more natural way,

to comprehend large sets of information (Table 3.64-Qh) without getting losing their

direction. The visual distinctions in Pivot are subtle, clear and effective (Table 3.63-Qd),

something which Tufte (1997) mentioned should be the design strategy, such that it

makes the ‘smallest effective difference’.

One of the most notable features of Pivot is the that it completely eradicates the

traditional ’10 blue links’ (Broder et al, 2010) hypertext environment, with a rather

exploratory search interface, a concept which has been always supported by many

authors (Broder et al., 2010, White, 2006, Hearst, 2009). Much of Pivot’s influence can be

said to be based on the FacetLens (Lee, 2009) and FacetMap (Smith, 2006) technology

that introduced multiple categories across which data could be visualised. To add to that,

Pivot used an improved and additional feature of Deep Zoom technology that allowed

users to smoothly and continuously navigate across data. In traditional Web based

interfaces, zooming or navigating to particular information required clicking between

individual HTML pages. However, studies showed that this often instigates the users to

lose sight of the context (Broder et al., 2010). However, with Pivot, the metadata could be

obtained in the same interface, without navigating away to a different Web page. But it

should be mentioned in this regard, that due to the nature of navigational and interactive

search, on an average, Pivot required more clicks to carry out the similar tasks as

compared to the traditional interfaces (Fig. 6.25, Table 6.30).



In addition, the overall experimentation results from the users indicated that the

smoothness and continuity provided by Pivot in navigating across the data, helped the

users to get a clear picture and understand what they were looking for and the also

realise how they reached there. Gary Flake, a Microsoft technical and director of Live

Labs, indicated that if there is a sudden transition, users lose their way; on the other

hand, if it is smooth and continuous, users tend to have a mental model of how to they

reached the particular location (Microsoft News Center, 2010).

According to the words of the director of reporting and digital analytics within

Microsoft’s global marketing organization, Michael Moore, “Pivot is a beautiful merge of

quantitative and qualitative analysis” which “facilitates a subjective analysis of the

underlying objective data”. However, one of the greatest drawbacks of PivotViewer is

that, the essential categories for the datasets are picked in advance and hence the system

is difficult to port to other data collections with different items, something that Smith et

al. (2006) found in a similar study for information visualisation. Also the facets may be

evolving from time to time, meaning continuous changes in the schema is required from

time to time. This may indicate the use of higher number of categories, which on the

downside may also overwhelm the users, due a cognitive overload as indicated by

Wilson and Schraefel (2008). But overall, Pivot offers an interface that makes sure that

the trends and patterns of information appear immediately to the user through an

experience that is not possible when data is stuck in hyperlinked pages or constituted in

the form of rows and columns.

7.3 Summary

In this Chapter the results obtained in the previous chapter has been thoroughly

discussed with respect to the background literature discussed in Chapter 2. Special care

was taken to the visualisation and retrieval aspect of the PivotViewer. The results clearly

indicated a shift in the appreciation of the users from traditional search interfaces to a

more interactive system, something that has been discussed, prototyped and improved

by many authors over the years. This piece of analysis gives a closer look at the potential

of PivotViewer as an interface which may open doors to novel and far efficient ways of

information searching on the Web in the future.



Chapter 8

8.1 Research Summary

The primary theme of this piece of research has been to study and explore the

techniques in Microsoft PivotViewer, to support information visualisation and

interaction on a set of data collections, and understand the potential advantages and

disadvantages that this interface may impose, as compared to the traditional interfaces,

through user-centred evaluations. To achieve this, a new prototype interface was

designed in PivotViewer, based on the St. Andrews University collection, and the system

was compared with the pre-existing and official interface available online. In addition,

two similar sets of experiments were carried out. One of the experiments was to evaluate

an open ended-search interface in Google Images and compare it with a Pivot interface

based on a particular search domain. The other was based on evaluating between the

traditional interfaces of IMDb and Netflix PivotViewer, both of which were operating in

the same domain. The experiments ensured a challenging and holistic evaluation of the

Pivot interface based on an array of defined performance criteria. The results gathered

from the study indicated a positive graph, illustrating the advantages of granting the

users a visually enriching experience in information retrieval, through interactive

manipulations and explorations.

In this chapter, the primary lists of objectives achieved are briefly discussed, and a

summary of results are illustrated. Finally, the limitations of this research are highlighted

and finally concluded with an outline of possible future research.

CONCLUSION



8.2 List of Objectives

The list of objectives described in Chapter 2 was closely followed in this research and

met successfully. This section summarises these achievements:

Aim -1

To carry out a study-oriented investigation of information retrieval and visualisation

systems, along with the exploration of associated user behaviour, in information searching.

Fulfilment of objectives:

In Chapter 3, detailed literature reviews were carried out in the areas of:


A study of traditional forms of information retrieval systems by various authors

were discussed in Section 3.1 (Chapter 3), followed by insights on Web-based

information searching. Information retrieval techniques, in large faceted datasets,

were also discussed, keeping in relation to the present study. Literature on web user

interfaces was also briefly discussed.

Information Visualisation

A brief introduction on information visualisation was provided and related studies

were discussed in Section 3.2, with certain emphasis on key theories. This was

followed by discussions and reviews on Web-based information visualisation by

eminent authors. Finally literature on visualisation of large faceted datasets was

reviewed and discussed.

User behaviour in information searching

Behaviour of users in information searching was discussed in section 3.3, along with

some references to key literature in this area.



Aim -2

Design and implement a Pivot collection with a large dataset.


Objective 1:

To study the techniques and features, to design and build pivot collections.

In Chapter 4, the key features and techniques used in designing, developing and

publishing a Pivot collection has been identified and discussed.

Objective 2:

To transform the historic photo collection available in St. Andrews University Library,

into a prototype Pivot collection.

In Chapter 5, the technique to implement a PivotViewer collection based on 10,000

images of St. Andrews University Library historic collection was successfully

implemented and tested with users.

Aim -3

Evaluation and analysis of Pivot interface compared to traditional Web interfaces for

information retrieval and visualisation.


Objective 1: To perform a comparative performance evaluation between the Pivot

interface developed on St. Andrews University photographic collection and the original

interface and explore the results.

The evaluation was successfully carried out as explained in Section 5.7.8, with a

detailed result analysed in Section 6.5.1.

Objective 2: To perform a comparative analysis and performance evaluation, on

image retrieval on a particular domain, between an open-ended interface (Google

Images), with that of a Pivot interface on a particular dataset.



Objective 3: To perform a comparative analysis and evaluation of two interfaces

dedicated to the similar domain of information retrieval service - IMDb and Netflix

PivotViewer interface.





8.3 Results

From the observations and tests carried out, PivotViewer emerged as a clear winner in

terms of speed, as well as the performance criteria set. The time taken by Pivot Viewer

was comparatively less (almost half) the time taken to do the same task in other

interfaces. However, the number of clicks in Pivot was slightly higher. In terms of

performance, PivotViewer scored higher than traditional interfaces in all the criteria:

satisfaction, usability, visual appeal, confidence, and results retrieved. Marginal

differences favouring Pivot more, compared to others, were in the area of satisfaction

and visual appeal; and the differences were closest in the area of confidence. Users

agreed highly to the fact that it was a novel experience, which helped them form a clear

mental picture of the collections. The interface also proved to be quick and quite easy to

use. Overall Pivot provided a rich multifaceted multimedia visual experience to the users

and proved to be more efficient and acceptable to the users as compared to the

traditional search interfaces.

8.4 Limitations

Given the scope of the project, several limitations have been identified:

The evaluation of the system has been based on the handful of user experiments

that has been carried out. It may not fully reflect the overall sentiment of a vast

majority of users. But care has been taken to include a varied set of users, and the

overall positive results indicate a success. However, it could have been better to

include a wide array of participants, to further affirm the results by achieving

saturation in result quality.

This project does not include the evaluation of the stand alone system and

measurement of internal technicalities, in terms of processing speed and

resource consumption levels, with that of the servers evaluated against. This is

due to lack of accessibility of the systems as well as the limited time.

The St. Andrews University Library Photographic collections had more number of

photo collections (around 30 thousand) as compared to the Pivot prototype

collection which had 10,000 pictures analysed. This is due to the lack of

resources available in publishing the magnitude of data on the local server

provided. However, though this may not indicate an ideal comparison situation,

the focus was more on the performance criteria of the interface, in terms of

delivering overall user experience.



The experiments were based on static data collections only. It would be

interesting to implement and evaluate dynamic features.

Colour schemes have not been incorporated in building the St. Andrews

Collection to visually distinguish data, due to lack of resources, which could

otherwise have been better. However, we have evaluated a complete interface

with proper imagery, through the other Pivot interfaces included in the test.

The quality of the images obtained from the St. Andrews Collection was not of a

very high resolution and hence the experience may not have been optimal.

8.5 Future Research

In this dissertation, an image retrieval interface has been successfully designed using

PivotViewer which proves to be a promising tool for the next-generation of user

interfaces. It allows users to navigate and manipulate the search criteria, to retrieve

information from large data collections, using faceted metadata flexibly. Despite the fact

that the interface was new to the users and they were not familiar in using it before, it

was strongly preferred by most of the participants of the experiments. This also

highlights the fact that, categorisation in image and information retrieval, along with

proper visualisation, is a successful approach, in the area of search and retrieval systems.

Studies in future need to be carried out to compare the effects and possibilities of

including custom personalisation of the interface, history, sharing and researching the

efficacy of the technology, on the probability of text collections. Besides, there are also

several areas of improvements that can be added and experimented in further research,

by introducing further clarity in establishing facet relationships by using colour schemes

and running the application on a higher configuration system with dedicated GPU.

Besides, further thoughts needs to be introduced in incorporating tools, specifically for

evaluating such systems, by keeping in mind, the challenges and problems in carrying

out proper evaluation for information visualisation systems (Plaisant, 2004; Rhyne,

2004), by ensuring qualitative and quantitative effectiveness. At the same time, it is also

required to carry out further iterations on the designing the system and enable in the

deployment of the system online to a wider array of users for further assessments, to

ensure a qualitative saturation of results.



Through this research, several additional features of improvements have been identified

that that could enrich the user experiences in information retrieval systems. For

example, currently there are no easy methods to gather information on all the research

work written by authors in the area of information visualisation in the UK (or around the

world), since publication works have mostly been distributed across several established

journal portals. It would be interesting to study and investigate ways, to bring together

all the papers, by scaling to a larger dataset, comparable to the combined repository of

databases, such as Emerald, ACM, IEEE and use URLs to directly link them to these sites

through PivotViewer after the users are able to slice and dice to obtain the required

paper. In this way, there could be a central repository of information resources that can

be accessible via the pre-existing systems without any infringement on the copyright

laws, but rather a joint effort. Besides, studies can also be carried out on organisations to

tap useful information and investigate the means to enable proper channels, to gather

business intelligence and evaluate it. This research has dealt with static datasets, in

future dynamic datasets needs to be implemented and assessed.

Several challenges are also expected in this research, one of which being the inclusion of

many facets. Apart from finding ways to handle many facets, an optimal technique to

chalk out meaningful and important facets needs to be also incorporated. Currently in

PivotViewer, there are no means for the users to save their search points and retrieve a

pre-existing filtered search state. It would be interesting to investigate the possibilities of

tweaking the Pivot SDK to include this additional feature, apart from allowing other

features such as history and sharing.

Due to the constraints and time limits for the project, internal system based evaluations

were not carried out for the project to assess the performance as compared to the pre-

existing system. In future study, a thorough internal evaluation is also proposed to be

carried out, that should properly analyse the technical specifications along with testing

the processing speeds while filtering large datasets. In order to achieve this, several

prototypes need to be designed, with variable and dynamic datasets, on several domains.

Proper overall evaluation on all the systems would help in achieving saturated results,

which can then be properly analysed and assessed to identify the risks and benefits, by

carrying out feasibility studies. This could also be successful set up interest groups in this

area, leading major players to carry out transformations in the way users access data

today, by bringing in the possibility of a next generation of experience in information

visualisation and manipulation of data collections.



Finally, it should be noted that technology is always shifting and moving from

proprietary architecture. There is a possibility of shift from plug-in architecture to a

more open kind of architecture with the introduction of HTML 5. Being a platform

specific product, there might be several challenges in future. It will be interesting to

study how Microsoft Corporation responds to this situation, and how overall we find a

further solution in this spectrum, for the generations to come.

8.6 Summary

In this dissertation, PivotViewer has been described as an interesting visualisation

interface that supports interactive exploration, enabling cognitive sense-making through

faceted data collections by highlighting trends and relationships within the contents. It

introduces a new level of interactivity and features that enable users to easily retrieve

the required information, identify and navigate across related patterns, and compare the

trends.

To highlight the utility of the interface, several insights gained were reported and

graphically analysed from participating users, who explored across three implemented

Pivot interfaces (one designed for the purpose of the project, two pre-existing in the Web

– Endangered Species sample PivotViewer from Microsoft and Netflix PivotViewer),

while each time comparing with a traditional interfaces (St. Andrews University Library

Photographic Collection, Google Images, IMDb). The results were also presented from a

formative user study taken to identify the performance and usability of the interface as

compared to the traditional interfaces. PivotViewer was quite innovative, enjoyable and

interactive to use, according to the users in exploring the tasks given and overall they

liked it and rated it highly. Finally, potential future work using the PivotViewer has been

presented.

From the study it was learnt that it is essential today, to allow users to follow their own

curiosity in knowledge searching. Allowing the users to swim through contents,

undertaking specific tasks or gathering data, based on some pre-defined criteria in mind

in a fast, easy and effective way in today’s fast-paced environment, is no longer an added

luxury but actually a necessity



Manual information gathering is all about finding a few items from the various

collections, making a choice and then acting on that choice based on the data available. If

the user is able to browse through huge amount of aggregated data, it is much easier and

interesting to browse through that data. At the same time, if it could be something visual

and easy to relate to, like PivotViewer, it could make information retrieval to a whole

new level. Besides, information is no longer the domain of just a few individuals but has

now spread to a completely new genre of business and market competition with the

growth of industries. Information retrieval and visualisation systems, such as

PivotViewer, are increasingly essential for business intelligence and to explore

interactive trends. The impact of information, through developers, designers and

business decision makers are effectively changing the way our world functions today.

With a tool such as PivotViewer, it is estimated that large volumes of data could be

interacted with, in such a way that it will bring in new insights, gathering more wisdom

from what was earlier obscure and mysterious, and enable an insight that will help users

to make better decisions as well as formulate improved policies. Since Pivot has the

technology to work with almost any data format. As such with an increase in the number

of potential users, the domain of its expansion could be as vast and varied as the

information available today, in other words, practically limitless.



APPENDIX

List of interesting Pivot applications

http://memorabilia.hardrock.com/

http://www.molbiol.ox.ac.uk/data.shtml

BioPivot – University of Oxford, UK http://www.molbiol.ox.ac.uk/data.shtml

Buxton Collection – Bill Buxton’s UX devices http://research.microsoft.com/en-

us/um/people/bibuxton/buxtoncollection/pivot.htm

Classic Cardboard – ProjectVerona http://www.classiccardboard.com/

Cricket Video Vault – Blackcaps http://www.blackcaps.co.nz/vault/

Flickr Demo – LobsterPot http://pivot.lobsterpot.com.au/flickr

percollate – social media dashboard http://www.percollate.com/Demo

PhotoPivot – organize your photo collection

http://www.photopivot.com/photopivot/

tweetpivot – pivot the twitterverse http://www.tweetpivot.com/

Microsoft BI Customers – BI > Solutions > Customers

http://www.microsoft.com/bi/en-us/Solutions/Pages/Customers.aspx

MVPChat – MVP Twitter Threads http://mvpchat.championds.com/

Netflix instant watch movies+ – Windows Azure Team

http://netflixpivot.cloudapp.net/

PivotViewer Interactive Demos – Official Microsoft Silverlight

http://www.microsoft.com/silverlight/pivotviewer/

The TEAM Project – UCSD and Conservation International

http://gis.team.sdsc.edu/teamimages/

Top SharePoint Internet Sites – WSSDEM: http://www.wssdemo.com/livepivot/

Windows Phone 7 Marketplace - WP7 App Hub: http://www.wp7apphub.com/

http://memorabilia.hardrock.com/



http://research.microsoft.com/en-us/um/people/bibuxton/buxtoncollection/pivot.htm

http://research.microsoft.com/en-us/um/people/bibuxton/buxtoncollection/pivot.htm

http://www.classiccardboard.com/

http://www.blackcaps.co.nz/vault/

http://pivot.lobsterpot.com.au/flickr

http://www.percollate.com/Demo

http://www.photopivot.com/photopivot/

http://www.tweetpivot.com/

http://www.microsoft.com/bi/en-us/Solutions/Pages/Customers.aspx

http://mvpchat.championds.com/

http://netflixpivot.cloudapp.net/

http://www.microsoft.com/silverlight/pivotviewer/

http://gis.team.sdsc.edu/teamimages/

http://www.wssdemo.com/livepivot/

http://www.wp7apphub.com/



Participant Consent Form

This form is based on a pro forma provided by The University of Sheffield. It was used to obtain

consent of volunteer participation for the experimentations undertaken.

Title of Project: Name of Researcher: Participant Identification Number for this project: Please initial box 1. I confirm that I have read and understand the information sheet/letter

(delete as applicable) dated [insert date] for the above project and have had the opportunity to ask questions.

2. I understand that my participation is voluntary and that I am free to withdraw

at any time without giving any reason. Insert contact number here of lead researcher/member of research team (as appropriate).

3. I understand that my responses will be anonymised before analysis. I give permission for members of the research team to have access to my anonymised responses.

4. I agree to take part in the above research project. ________________________ ________________ ____________________ Name of Participant Date Signature (or legal representative) _________________________ ________________ ____________________ Name of person taking consent Date Signature (if different from lead researcher) To be signed and dated in presence of the participant _________________________ ________________ ____________________ Lead Researcher Date Signature To be signed and dated in presence of the participant Copies: Once this has been signed by all parties the participant should receive a copy of the signed and dated participant consent form, the letter/pre-written script/information sheet and any other written information provided to the participants. A copy for the signed and dated consent form should be placed in the project’s main record (e.g. a site file), which must be kept in a secure location.



Participant Information Sheet

The aim of this research is to carry out an investigation of information retrieval and

visualisation systems, along with an exploration of user behaviour in information

searching. Secondly it is aimed to design and implement a Pivot collection with a large

dataset and finally carry out a comparative evaluation and analysis of Pivot interface

with respect to traditional Web interfaces for information retrieval and visualisation.

The aim of the lab session you have been invited to participate in today is to assess the

functionality and ease of use of three PivotViewer interfaces as compared to three

traditional image and information search interfaces that allow users to browse and

navigate for photos and information. The feedback you give will be used in the

assessment of PivotViewer interface as a visually interactive information retrieval

system.

The evaluation will consist of an opening questionnaire, to record some personal

characteristics (such as age, education background etc.) and your previous experience

using the internet and search engines.

Following brief instructions from the researcher, you will then be invited to familiarise

yourself with the software being evaluated, before undertaking some suggested search

tasks. Most of these tasks have no right or wrong solution, their aim is to assess how

you make use of the software.

Finally, you will be asked to complete a post evaluation questionnaire, giving your

opinion of the software.

Participation in this evaluation is entirely optional. You may choose not to answer any

question or participate in any task. You may also choose to withdraw from the

evaluation at any time.

All data collected in the evaluation will be completely anonymised (i.e. you will not be

personally identifiable from the data). To take note of the time and number of clicks in

certain areas of the evaluation, special software shall be used. All associated data

collected in this evaluation, will be deleted within one year of the project’s completion.

If you have any concerns during or after the evaluation, you may talk to the researcher

Souvik Dey ([email protected]) or to the project supervisor, Dr Paul Clough

([email protected], Tel 0114 222 2664).

Many thanks for your time and co-operation, which is greatly appreciated.

- Souvik Dey



1. Preliminary Questionnaire I - Basic Details



II – General Familiarity





III - Evaluation of user-centred expectations

IV - Pivot Awareness



2. User–centred Interface Evaluation

I. Scenario – A: St. Andrews University Library Photo Collection

i. Task list for Implemented Interface



ii. Evaluation of Scenario – A:



II - Scenario – B:

i. Task list for Scenario B (Pre-existing interface-1) Open ended browsing/retrieval with Google Images vs. Pivot Collection on endangered species.



ii. Evaluation of Scenario – B:



III - Scenario – C:

i. Task list for Scenario C (Pre-existing interface-2)

Close ended visual information retrieval on movies using IMDB vs. Netflix Pivot Collection:



ii. Evaluation of Scenario – C:



3. Post-experimentation questionnaire

i. Part 1



ii. Part 2



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