Information Visualization and Visual Analytics

Pekka Wartiainen

University of Jyvaskyla

pekka.wartiainen@jyu.fi

23.4.2014

Outline

Objectives

Introduction

Visual Analytics

Information Visualization

Our research

Summary

Learning objectives

I To understand the definition of visual analytics.

I To be aware with visual analytics approach in problem solving.

I To understand the basics of data visualization.

Motivation

I Raw data has no value in itself, only the extracted informationhas value

I Time and money are wasted and opportunities are lost

I Success depends on availability of the right information

I Visual analytics aims at making data and informationprocessing transparent

I Visual analytics combines the strengths of humans andcomputers

An historical perspective on visual analytics

I Early visual analytics: exploratory data analysis

I Visual data exploration and visual data mining

I First book of visual analytics: Illuminating the Path, 2005

I Some earlier systems exhibited the characteristics of visualanalytics

I CoCo system for improving silicon chips, 1990

Past few years

I VisMaster is an European Coordination Action Project

I Web-page:

I URL: Visual-Analytics.EU

I Book:

I URL: Mastering the information age - solving problems withvisual analytics

I YouTube video:

I URL: Inria - Vismaster, visual analytics

Visual analytics

DefinitionVisual analytics combines automated analysis techniques withinteractive visualisations for an effective understanding, reasoningand decision making on the basis of very large and complexdatasets.

Timeline

Application of visual analytics

I First application area was security

I Many major application areas

I physics, astronomy, medicine, climate, . . .

Example: business intelligence

I Financial market generates large amounts of data on a dailybasis–> extremely high data volumes over the years

I More than 300 million VISA credit card transactions per day

I Multiple perspectives and assumptions for analysis

I history, current situation, monitoring, forecasting, recurringsituations

Visual analytics – Coordinated Graph Visualization

Visual support for the simulation of climate models provided byCGV, a highly interactive graph visualization system.

Visual analytics – NFlowVis

Analysis of a distributed network attack.

The visual analytics process

Process model of visual analytics.

Building blocks of visual analytics research

Visual analytics integrates science and technology from manydisciplines.

Evaluation

I Evaluation include techniques, methods, modes and theoriesas well as software tools

I Challenge: often processing data from the real world

I Evaluation involves users, tasks and data

I Especially in the industry, the domain expert has the bestknowledge–> Empirical evaluation

I Evaluation criteria, e.g.:

I effectivenessI efficiencyI user satisfaction

I Importance of documentation is emphasized

Infrastructure

I Visual analytics is both user-driven and data-driven

I Current challenges: lack of interaction and dynamic data

I Limitations of traditional data bases

I Old fashioned ‘architectural reference model’I Big data solutions

I Need for:

I Fast imprecise answers with progressive refinementI Incremental re-computation, either in the data (e.g., some

data has been changed) or in the analysis parametersI Steering the computation towards data regions that are of

higher interest to the user.

Data management – Why?

I The big opportunity of the Information Age

I Many obstacles need to be overcome

I Heterogeneity of data sourcesI Different data typesI Data streamsI Working under pressureI Time consuming activities

I Data management ensures data consistency and standards

Data management – VA aspects

I Data and semantic integration

I Utilizing known processing methods

I Data Warehousing, OLAP and Data Mining

I Data reduction and abstraction

I Data quality is crucial (cf. GIGO model)

I Visual techniques for exploring data

Space and time

I In large systems, space and time are essential–> complexity increases

I Space and time are more than just numbers

I Specific properties:

I Dependencies between observationsI UncertaintyI ScaleI Time

I Spatial approaches: Cartography, GIS, Geovisualization

I Representation of time: visualization of time-related data andtime itself

I Interactive visualizations

I Big data cases – dimension reduction

Space and time – OECD eXplorer

Allows to explore regional statistics data from OECDURL:Organisation for Economic Cooperation and Development

Data mining

I Humans are required in the data analysis process

I New tools and methodologies are necessary to help expertsextract relevant information

I Limitations in KDD process and visualizations

I Combination of multidisciplinary approaches

I Pattern identification methods

I Spatio-temporal data mining

I Many software have been developed

Perception and cognitive aspects – visualization

I The human is at the heart of visual analytics humaninteraction, analysis, intuition, problem solving and visualperception.

I Distinction between high and low-level vision

I Humans do not have to remember everything but extractvisual clues from the environment

Pre-attentive processing makes items pop out the displayautomatically.

Data visualization

I Fast and understandable way to present data to a user

I Data mining methods as pre-processing tools

I Many visualization methods existing

I JFreeChartI Google Charts

I Remember how not to use visualization techniques

I Dynamic behavior of the data sets special requirements

I Data visualization is part of information visualization

GUI design

I Visual analytics has high demand for GUI

I Scalable and interactive interface

I General guidelines for different purposes

I Windows, OS X, Android, . . .I Online solutions

I Define target group before designing the GUI

I Multidisciplinary research groupsI Personalized user roles

Common interaction

I select : mark data items of interest, possible followed byanother operation,

I explore : show some other data e.g., panning, zoom,resampling,

I reconfigure : rearrange the data spatially e.g., sort, changeattribute assigned to axis, rotate (3D), slide,

I encode : change visual appearance e.g., change type ofrepresentation (view), adjust colour/size/shape,

I abstract/elaborate : show more or less detail e.g., details ondemand, tooltips, geometric zoom,

I filter : select or show data matching certain conditions,

I connect : highlight related data items e.g., brushing (selectionshown in multiple views).

Using colors

I Powerful element in visualization

I Wrong usage of colors is disturbing

I Color Usage Research Lab

I NASA Ames research center

I Ready made color palettes are solid alternatives

Visual analytics in energy production

I Application area: BFB boiler burning biomass

I Co-operation with VTT, department of chemistry, and privatecompanies

I Funded by Regional Council of Central Finland

I Time-series data measured from the different parts of theprocess

I Context-sensitive framework approach

I Matlab routines with Java GUI

People included into process

The human context of visual analytics.

Summary

I Visual analytics for multidisciplinary research problems

I Visualization, data analysis, user interaction

I Highly interactive interfaces

I The whole process should be taken into account

I Many challenges still existing, especially with big and dynamicdata

I Humans are part of the process

References

D. Keim, J. Kohlhammer, G. Ellis ja F. Mansmann, Mastering theInformation Age: Solving Problems with Visual Analytics, EurographicsAssociation, Germany, 2010.

P. Jarvinen, K. Puolamaki, P. Siltanen ja M. Ylikerala, Visual Analytics,Technical report, VTT, Finland, 2009.

P. Wartiainen, T. Karkkainen, A. Heimburger, ja S. Ayramo.Context-sensitive approach to dynamic visual analytics of energyproduction processes. In 22th European-Japanese Conference onInformation Modelling and Knowledge Bases. MATFYZPRESS -Univerzity Karlovy, 2012.