Information Visualization & Visual Analytics
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1 Information Visualization & Visual Analytics Jack van Wijk Dept. Math. & Computer Science TU Eindhoven BPM round table, March 28, 2011
2 Overview InfoVis Visual Analytics
3 Why is my hard disk full??
4 SequoiaView Van Wijk et al., 1999, Bruls et al. 2000
5 Information Visualization The use of computer-supported, interactive, visual representations of abstract data to amplify cognition (Card et al., 1999) Abstract dataset (table, graph, tree) data Information Visualizatio n image User interaction
6 Abstract data Multivariate data visualization scatterplot Tree visualization tree diagram Graph visualization node link diagram
7 InfoVis at TU/e Focus: Large data sets, professional users Use of computer graphics know-how shading, geometry, texture, Software Visualization (similar issues as BPM?)
8 Software Visualization User: developer, architect, manager, Some fuzzy questions: Is the structure sound? Can I improve the structure by refactoring? What has happened with the system? Does the implementation conform the architecture? Where are the weak spots?
9 Different views on software Architecture System structure Data Coordination, temporal aspects Code Structure Derived data, metrics Evolution Execution Traces, call graphs
10 Challenges in Software Visualization Combination of large amounts of Multivariate data (metrics) Hierarchical data (system, subsystem, module,..) Graph data (call relations) Text (names, code) + + =
11 Trees + graphs Ubiquitous!
12
13
14 MatrixView Data: hierarchy of layers, units, modules, classes, methods methods calling each other
15 MatrixView Matrix representation of graph B A C E D A B C D E A B C D E
16 MatrixView Van Ham 2003, Van Wijk et al., 2003
17 Hierarchical Edge Bundles Again, tree+graph, but now completely different Holten, 2006
18 Showing directions in edges arrow light-to-dark dark-to-light green-to-red curved tapering Holten et al., 2009
19 Result of experiments
20 Visual Analytics: Beyond visualization
21 Origin Founder: Jim Thomas, NVAC Illuminating the Path, 2004 Visual Analytics: The science of analytical reasoning facilitated by interactive visual interfaces
22 Definition The science of analytical reasoning facilitated by interactive visual interfaces Compact! Complete! Perfect! But what is it?
23 Video VisMaster
24 An InfoVis perspective Abstract dataset (table, graph, tree) data Information Visualization image User interaction
25 An InfoVis perspective data management statistics mathematics design art Many, large, heterogenous datasets - gigabytes, terabytes, petabytes - statistics, machine - domain learning, expertise - tables, images, Data mining documents, pattern videos, recognition, audio, - fit in workflow artificial intelligence, - from data foraging to presentation - teamwork Abstract dataset (table, graph, tree) data Information Visualization image Professional User interaction software engineering graphics HCI perception cognitive psychology
26 The key ingredients Huge, heterogenous data sets Integration of data mining and visualization Integration in workflow Support for all stages of data analysis Support for multiple users Keyword: INTEGRATION Result = product of parts (2 x 2 x 2 x 2 x 2 = 32)
27 FAQ We know this already, isn t it just: applied infovis, visual data mining, visual data analysis, statistical graphics, Sure, Visual Analytics builds on existing technologies and earlier examples exist
28 One year of time-series data #people at work 365 graphs 0:00 12:00 24:00 Van Wijk et al., 1999
29 After clustering #people at work 365 graphs 0:00 12:00 24:00 Van Wijk et al., 1999
30 Command Post of the Future Steven Roth et al. Visage (1996), CoMotion, MAYA Viz Interaction, heterogenous data, knowledge sharing, teamwork, decision making,
31 FAQ We know this already, isn t it just: applied infovis, visual data mining, visual data analysis, statistical graphics, Sure, Visual Analytics builds on existing technologies and earlier examples exist but integrating all of these is still novel, difficult, and challenging.
32 FAQ This Visual Analytics, that s American, right? No, wrong.
33 EU-funded Coordination Action Project 26 partners, 12 countries Developing roadmap Organizing events Communication platform Video (youtube: vismaster) Daniel Keim Jörn Kohlhammer
34 Visual Analytics: Great! Big! Challenging! Summary
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