1 Information Search and Visualization Information Terminology Information Retrieval Information gathering, seeking, filtering, and visualization

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Information Search and Visualization

Information Terminology

Information Retrieval

Information gathering, seeking, filtering, and visualization

Task objects: e.g., video clips, documents

Task actions: browsing and searching

Interface actions: Scrolling, joining, zooming, linking

Database Management – refers to structured relational database systems, well defined attributes and sort-keys

Data mining, data warehouses, data marts

Knowledge networks, semantic webs

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Information Terminology

Specific fact finding: known-item search

• Example: find the email address of Keith Jackson Extended fact finding

• Example: What are the sonnets by Shakespeare Exploration of availability

• Example: Is there new work in process control published by IEEE Open ended browsing and problem analysis

• Is there new research on the use of cell phones in China

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Searching in Text Documents and Database Querying

Google’s Link Based Ranking Measure – PageRank (Brin & Page, 1998)

• Computes a query independent score for each document

• Takes into consideration the importance of the pages that point to a given page

• The big dogs know where to hunt SQL (database query language)

• Example:

SELECT DOCUMENT#

FROM JOURNAL = MY_FAVORITE_JOURNAL

WHERE (DATE > 2001 AND DATE <= 2003)

AND (LANGUAGE = ENGLISH)

AND (PUBLISHER = HFES OR ACM) Natural Language Queries

• Mainly just eliminates frequent terms

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Form-Fillin Queries (http://thomas.loc.gov/)

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Phases of search

• Formulation: expressing the search

• Initiation of action: launching the search

• Review of results: reading messages and outcomes

• Refinement: formulating the next step

• Use: compiling or disseminating information

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Formulation

• Identify the source of the information (e.g., within a specific library)

• Use fields to limit the search (e.g., year or language)

• Recognize phrases to allow entry of names (e.g., Abraham Lincoln)

– Allow for search my phrase or individual items in the phrase

• Apply variants to relax the search constraints

– Case sensitivity (JEFFERSON, Jefferson)

– Stemming (sing, singing)

– Partial matches (biology, psychobiology, sociobiology)

– Phonetic variations (Smith, Smyth, Smythe)

– Abbreviations (ATT, NCR)

– Synonyms (West Coast retrieves Washington, Oregon and California)

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Formulation

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Initiation of Action

• Explicit initiation (e.g., search button)

• Implicit initiation: each change to a component of the formulation phase

immediately produces a new set of search results (e.g., Google)

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Review of Results

• Users can read messages and view textual lists

• Allow the user to control

– The number of results

– Which fields are displayed

– The sequence of the results

– How results are clustered

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Review of Results

• Clustering

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Review of Results

• User control

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Refinement

• In the event of few results, indicate that using fewer search criteria, or partial matches may increase the number of hits

• Suggested spellings

• If no results are found, always provide users with that information

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Use Results

• Merge, save, distributed via email, output to visualization programs, or statistical tools

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Multimedia Document Searches

Most systems used to locate images, video, sound and animation depend on metadata

Example: search of a photo library by date, photographer or text captions

• Requires significant human effort to add captions and annotate Image search: query by image content

Map search

• Search by latitude and longitude

• Search by features (e.g., search for all cities in northwest United States with airports)

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Picasa

Supports browse and search of photos in public albums

Automatically organizes the user’s online photo collection based to who's in each picture

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Other Searching Mechanisms

Sound Search – Music-information retrieval (MIR)

• Users can play or sing as input, and matching songs will be returned Video Search

• Segment into scenes

• Allow scene skipping Animation Search

• Examples: search for morphing faces

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Video Search

Informedia

Designed at CMU to solve the problem of searching huge collections of video and audio recordings

Developed new approaches for automated video and audio indexing, navigation, visualization, search

Provides full-content search and retrieval of current and past TV and radio news and documentary broadcasts.

Generates various summaries for each story segment: headlines, filmstrip story-boards and video-skims

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Video Search - Informedia

Example: 12 documents returned for "El Niño" query along with different multimedia abstractions from certain documents

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Advanced Filtering and Search Interfaces

Filtering with complex Boolean queries

• Example: List all employees who live in Denver and Detroit

• Would most likely result in a null result since “and” implies intersection

• Most employees do not live in both locations

• Other approaches

– Venn Diagrams

– Decision Tables

– Metaphors of water flowing through a series of filters Automatic Filtering

• Selective dissemination of information

• Filtering email before it is placed in the Inbox

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Decision Table

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Dynamic queries

• Uses direct manipulation objects

http://www.bluenile.com/build-your-own-diamond-ring?first_step=diamond&forceStep=DIAMONDS_STEP

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Metadata search (e.g., Flamenco)

• Attribute values are selected by the user

• http://flamenco.berkeley.edu/demos.html

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Collaborative Filtering

• Users work together to define filtering criteria in large information spaces

• Example: If you ranked five movies highly, the algorithm provides you with a list

of other movies that were rated highly by people who liked your five movies Visual Searches

• Examples: Selecting dates on calendars or seats from a plane image

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Advanced Filtering and Search Interfaceshttp://www.mediabistro.com/10000words/what-is-a-treemap-5-examples-and-how-you-can-create-one_b736

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Information Visualization

The use of interactive visual representations of abstract data to amplify cognition

Scientific Visualization: requires two dimensions because typical questions involve

• Continuous variables

• Volumes Information Visualization involve

• Categorical variables

• Discovery of patterns

• Trends

• Clusters

• Outliers

• Gaps in data

http://www.youtube.com/watch?v=xekEXM0Vonc

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Uses human perceptual abilities to make discoveries, decisions and propose explanations

Users can scan, recognize and recall images quickly

Users can detect changes in size, color, shape, movement and texture

IV Rule

• Overview first

• Zoom and filter

• Details on demand

http://www.youtube.com/watch?feature=fvwrel&v=RgA4aaEfgPQ&NR=1http://www.google.com/

publicdata/directory

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1D Linear Data

• Text documents, dictionaries

• Organized sequentially

• Example: view 4000 lines of

code

• Newest lines are in red,

oldest lines in blue

• Browser window shows code

overview and detail window

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1D Linear Data

• All the words in Alice in

Wonderland, arranged in an

arc, starting at 12:00

• Lines are drawn around the

outside, words around the

inside

• Words that appear more

often are brighter

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2D Map Data

• Planar data include geographic

maps

• Each item has task domain

attributes, (e.g., name)

• Each item has interface

features (e.g., size or color)

• User tasks (find adjacent

items, regions containing

items, paths between items

•Proximity indicates similarity of topics

•Height reflects the number of documents

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3D World Data

• Real world objects –

molecules, human body,

buildings and the relationships

between the objects

• Users work with continuous

variables (e.g., temperature)

http://www.youtube.com/watch?v=8Ez6UQ69iQ0

http://www.youtube.com/watch?v=rcuq2eyuqHQ&feature=autoplay&list=ULOnYSHQumfro&playnext=1

http://www.youtube.com/watch?v=jbkSRLYSojo

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Multidimensional data

Extracted data from statistical databases

Tasks include finding patterns, correlations between pairs of variables, clusters, gaps and outliers

•www.inxight.com•Example of listing of houses for sale•Spreadsheet metaphor

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Multidimensional data

• Hierarchical or k-means

clustering to identify similar

items

• Hierarchical: identifies close

pairs of items and forms ever-

larger clusters until every

point is included in the cluster

• K-means: starts when users

specify how many clusters to

create, then the algorithm

places every item into the

most appropriate cluster

•http://www.cs.umd.edu/hcil/bioinfovis/hce.shtml•Example: hierarchical clustering of gene expression data• Identifying clusters of genes that are activated with malignant as opposed to benign melanoma (skin cancer)

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Temporal Data

Illnesses, Vaccinations, Surgeries, Lab Results

Events have a start/end time, and items may overlap

Tasks: finding all events before, after or during some time period or moment

•www.cs.umd.edu/hcil/lifelines•Example: Patient Medical Record

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Tree Data

• Collection of items where each

item has a link to one parent

item

Example: Organization Chart

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Tree Data

• Hyperbolic Tree Structure

• Limit the number of nodes in the center of the UI

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TreeMap

• Each rectangle represents a

stock and are organized by

industry groups

• The rectangle is proportional

to the market capitalization

• The color indicates gain/loss

• “N” indicates a link to a news

story

Map of the Market http://www.marketwatch.com/tools/stockresearch/marketmap

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Social Network Data

• When items are linked to an

arbitrary number of other

items

• Users often want to know the

shortest or least costly path

connecting two items

Facebook Data Visualization toolshttp://www.toprankblog.com/2010/08/6-facebook-search-engine-data-visualization-tools/

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Facebook: Social Graph

Facebook: Friend Wheel

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Parallel Coordinates

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Star Plots

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Information Visualization Overview Task

• Users can get a overview of the entire collection

• Zoom

• Detail View

Filter Task

• Users can filter-out items that are not of interest

Details-on-demand Task

• Users can select an item or group to set details

Relate Task

• Users can relate items or groups within a collection

• Show relationships by proximity, containment, connection or color coding

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History Task

• Supports undo, replay and progressive refinement Extract Task

• Allows extraction of sub-collections

• Send items are obtained

– Save

– Email

– Insert to a statistical package

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Periodic table of data visualization methods

Web Site