1. ComputingIntroVAGraphicsInteractionWrap-up 1/33 Data Exploration, Analysis, and Representation: Integration through Visual Analytics Remco Chang, PhD UNC Charlotte Charlotte Visualization Center

2. ComputingIntroVAGraphicsInteractionWrap-up 2/33 Problem Statement The growth of data is exceeding our ability to analyze them. The amount of digital information generated is growing exponentially… – 2002: 22 EB (exabytes, 10 18 ) – 2006: 161 EB – 2010: 988 EB (almost 1 ZB) 1: Data courtesy of Dr. Joseph Kielman, DHS 2: Image courtesy of Dr. Maria Zemankova, NSF

3. ComputingIntroVAGraphicsInteractionWrap-up 3/33 Problem Statement The data is often complex, ambiguous, noisy. Analysis of which requires human understanding. – About 2 GB of data is being produced per person per year – 95% of the Digital Universe’s information is unstructured There isn’t enough man-power to analyze all the data, and the problem is getting worse! Solution: help the user – Find patterns – Filter out noise – Focus on the important stuff 1: Data courtesy of Dr. Joseph Kielman, DHS 2: Image courtesy of Dr. Maria Zemankova, NSF

4. ComputingIntroVAGraphicsInteractionWrap-up 4/33 Example: What Does (Wire) Fraud Look Like? Financial Institutions like Bank of America have legal responsibilities to report all suspicious wire transaction activities (money laundering, supporting terrorist activities, etc) Data size: approximately 200,000 transactions per day (73 million transactions per year) Problems: – Automated approach can only detect known patterns – Bad guys are smart: patterns are constantly changing – Data is messy: lack of international standards resulting in ambiguous data Current methods: – 10 analysts monitoring and analyzing all transactions – Using SQL queries and spreadsheet-like interfaces – Limited time scale (2 weeks)

5. ComputingIntroVAGraphicsInteractionWrap-up 5/33 WireVis: Financial Fraud Analysis In collaboration with Bank of America – Develop a visual analytical tool (WireVis) – Visualizes 7 million transactions over 1 year – Currently beta-deployed at WireWatch Uses interaction to coordinate four perspectives: – Keywords to Accounts – Keywords to Keywords – Keywords/Accounts over Time – Account similarities (search by example) R. Chang et al., Scalable and interactive visual analysis of financial wire transactions for fraud detection. Information Visualization,2008. R. Chang et al., Wirevis: Visualization of categorical, time-varying data from financial transactions. IEEE VAST, 2007.

6. ComputingIntroVAGraphicsInteractionWrap-up 6/33 WireVis: A Visual Analytics Approach Heatmap View (Accounts to Keywords Relationship) Strings and Beads (Relationships over Time) Search by Example (Find Similar Accounts) Keyword Network (Keyword Relationships)

7. ComputingIntroVAGraphicsInteractionWrap-up 7/33 Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces [Thomas & Cook 2005] Introducing Visual Analytics Since 2004, the field has grown significantly. Aside from tens to hundreds of domestic and international partners, it now has a IEEE conference (IEEE VAST), an NSF program (FODAVA), and a forthcoming IEEE Transactions journal. Graphics & Visualization Computing Interaction & Reasoning

8. ComputingIntroVAGraphicsInteractionWrap-up 8/33 Visual Analytics, A Graphics Perspective

9. ComputingIntroVAGraphicsInteractionWrap-up 9/33 Visual Analytics, A Graphics Perspective Master’s Thesis -- – Simulating dynamic motion based on kinematic motion Jiggling of muscles – Skinnable Mesh Volumetric deformation – Compared 3 types of mass- spring systems Regular (unconstrained) mass- spring Reduced degree of freedom Approximate finite element method with implicit integration Is this applicable beyond graphics and simulation? R. Chang, Simulation Techniques for Deformable Animated Characters. Master’s Thesis, Brown University, 2000.

10. ComputingIntroVAGraphicsInteractionWrap-up 10/33 From Graphics to Visual Analytics: An Example in Urban Simplification (left) Original model, 285k polygons (center) e=100, 129k polygons (45% of original) (right) e=1000, 53k polygons (18% of original) R. Chang et al., Legible simplification of textured urban models. IEEE Computer Graphics and Applications, 28(3):27–36, 2008. R. Chang et al., Hierarchical simplification of city models to maintain urban legibility. ACM SIGGRAPH 2006 Sketches, page 130, 2006.

11. ComputingIntroVAGraphicsInteractionWrap-up 11/33 Urban Simplification Which polygons to remove? Original ModelSimplified Model using QSlim Our Textured ModelOur Model Visually different, but quantitatively similar!

12. ComputingIntroVAGraphicsInteractionWrap-up 12/33 Urban Simplification The goal is to retain the “Image of the City” Based on Kevin Lynch’s concept of “Urban Legibility” [1960] – Paths: highways, railroads – Edges: shorelines, boundaries – Districts: industrial, historic – Nodes: Time Square in NYC – Landmarks: Empire State building

13. ComputingIntroVAGraphicsInteractionWrap-up 13/33 Algorithm for Preserving Legibility Paths & Edges – Hierarchical (single- link) clustering Nodes – Merging clusters – Polyline simplification using convex hulls Landmarks – Pixel-based skyline preservation That’s pretty good, right?

14. ComputingIntroVAGraphicsInteractionWrap-up 14/33 Urban Visualization with Semantics How do people think about a city? – Describe New York… Response 1: “New York is large, compact, and crowded.” Response 2: “The area where I live has a strong mix of ethnicities.” Geometric,Information,View Dependent (Cognitive)

15. ComputingIntroVAGraphicsInteractionWrap-up 15/33 Urban Visualization Geometric – Create a hierarchy of shapes based on the rules of legibility Information – Matrix view and Parallel Coordinates show relationships between clusters and dimensions View Dependence (Cognitive) – Uses interaction to alter the position of focus R. Chang et al., Legible cities: Focus-dependent multi-resolution visualization of urban relationships. IEEE Transactions on Visualization and Graphics, 13(6):1169–1175, 2007

16. ComputingIntroVAGraphicsInteractionWrap-up 16/33 Probe-based Interface Using Probes allows for comparing multiple regions-of-interest simultaneously R. Chang et al., Multi-focused geospatial analysis using probes. Visualization and Computer Graphics, IEEE Transactions on, 14(6):1165– 1172, Nov.-Dec. 2008.

17. ComputingIntroVAGraphicsInteractionWrap-up 17/33 Urban Visualization Graphics + Visual Analytics Applying graphics approaches – Data transformation (clustering, LOD, simplification) – Screen-based metrics – Hardware acceleration Applying visual analytics principles – Multi-dimensional data representation – Interactive exploration – Broader applicability

18. ComputingIntroVAGraphicsInteractionWrap-up 18/33 Extending Visual Analytics Principles Global Terrorism Database – With University of Maryland – Application of the investigative 5 W’s Bridge Maintenance – With US DOT – Exploring subjective inspection reports Biomechanical Motion – With U. Minnesota and Brown – Interactive motion comparison methods Where When Who What Original Data Evidence Box R. Chang et al., Investigative Visual Analysis of Global Terrorism, Journal of Computer Graphics Forum, 2008.

19. ComputingIntroVAGraphicsInteractionWrap-up 19/33 Extending Visual Analytics Principles R. Chang et al., An Interactive Visual Analytics System for Bridge Management, Journal of Computer Graphics Forum, 2010. To Appear. Global Terrorism Database – With University of Maryland – Application of the investigative 5 W’s Bridge Maintenance – With US DOT – Exploring subjective inspection reports Biomechanical Motion – With U. Minnesota and Brown – Interactive motion comparison methods

20. ComputingIntroVAGraphicsInteractionWrap-up 20/33 Extending Visual Analytics Principles R. Chang et al., Interactive Coordinated Multiple-View Visualization of Biomechanical Motion Data, IEEE Vis (TVCG) 2009. Global Terrorism Database – With University of Maryland – Application of the investigative 5 W’s Bridge Maintenance – With US DOT – Exploring subjective inspection reports Biomechanical Motion – With U. Minnesota and Brown – Interactive motion comparison methods

21. ComputingIntroVAGraphicsInteractionWrap-up 21/33 Human + Computer A Mixed-Initiative Perspective Our approach is great and successful! But it’s mostly user-driven… Human vs. Artificial Intelligence Garry Kasparov vs. Deep Blue (1997) – Computer takes a “brute force” approach without analysis – “As for how many moves ahead a grandmaster sees,” Kasparov concludes: “Just one, the best one” Artificial Intelligence vs. Augmented Intelligence Hydra vs. Cyborgs (2005) – Grandmaster + 1 chess program > Hydra (equiv. of Deep Blue) – Amateur + 3 chess programs > Grandmaster + 1 chess program 1 How to systematically repeat the success? – Unsupervised machine learning + User – User’s interactions with the computer 1. http://www.collisiondetection.net/mt/archives/2010/02/why_cyborgs_are.php Computer Process (Translate) Human

22. ComputingIntroVAGraphicsInteractionWrap-up 22/33 Human + Computer: Dimension Reduction – Lost in Translation Dimension reduction using principle component analysis (PCA) Quick Refresher of PCA – Find most dominant eigenvectors as principle components – Data points are re-projected into the new coordinate system For reducing dimensionality For finding clusters For many (especially novices), PCA is easy to understand mathematically, but difficult to understand “semantically”. age height GPA 0.5*GPA + 0.2*age + 0.3*height = ?

23. ComputingIntroVAGraphicsInteractionWrap-up 23/33 Human + Computer: Exploring Dimension Reduction: iPCA R. Chang et al., iPCA: An Interactive System for PCA-based Visual Analytics. Computer Graphics Forum (Eurovis), 2009.

24. ComputingIntroVAGraphicsInteractionWrap-up 24/33 Human + Computer: Comparing iPCA to SAS/INSIGHT Results – Users seem to understand the intuition behind PCA better – A bit more accurate – Not faster – People don’t “give up” Overall preference – Using letter grades (A through F) with “A” representing excellent and F a failing grade. Problem is worse with non-linear dimension reduction A lot more work needs to be done…

25. ComputingIntroVAGraphicsInteractionWrap-up 25/33 Human + Computer: User Interactions Capture a user’s interactions in a visual analytics system Translate the interactions into something that would affect the computation in a meaningful way Computer Process (Translate) Human Challenge: Can we capture and extract a user’s reasoning and intent through capturing a user’s interactions?

26. ComputingIntroVAGraphicsInteractionWrap-up 26/33 What is in a User’s Interactions? Goal: determine if a user’s reasoning and intent are reflected in a user’s interactions. Analysts Grad Students (Coders) Logged (semantic) Interactions Compare! (manually) Strategies Methods Findings Guesses of Analysts’ thinking WireVis Interaction-Log Vis

27. ComputingIntroVAGraphicsInteractionWrap-up 27/33 What’s in a User’s Interactions From this experiment, we find that interactions contains at least: – 60% of the (high level) strategies – 60% of the (mid level) methods – 79% of the (low level) findings R. Chang et al., Recovering Reasoning Process From User Interactions. IEEE Computer Graphics and Applications, 2009. R. Chang et al., Evaluating the Relationship Between User Interaction and Financial Visual Analysis. IEEE Symposium on VAST, 2009.

28. ComputingIntroVAGraphicsInteractionWrap-up 28/33 User Interactions, A Computational Approach Now that we’ve shown that (interaction ~= reasoning ) – Can we automate the process? Consider each of a user’s interactions as a fixed-length vector (Design Galleries [Marks et al. Siggraph 97]). User interaction in the left application can be represented as a single dimensional vector User interaction in the right application can be represented as a two dimensional vector Computer Process (Translate) Human

29. ComputingIntroVAGraphicsInteractionWrap-up 29/33 Conclusion Visual Analytics is a growing new area that is looking to address some pressing needs – Too much (messy) data, too little time By integrating interaction, graphics, and data computation, we have demonstrated that – There are some great benefits – But there are also some difficult challenges With great challenges come great opportunities… – Government agencies – Industrial partners Graphics & Visualization Computing Interaction & Reasoning

30. ComputingIntroVAGraphicsInteractionWrap-up 30/33 Future Work (Funded Projects) NSF SciSIP: – Title: A Visual Analytics Approach to Science and Innovation Policy. PI: William Ribarsky, Co-PIs: Jim Thomas, Remco Chang, Jing Yang. $746,567. 2009-2012 (3 years). – Abstract: developing metrics and visual tools for identifying patterns in science policies. NSF/DOD (Minerva Initiative): – Title: Collaborative Project: Terror, Conflict Processes, Organizations, & Ideologies: Completing the Picture. PI: Remco Chang $100,000. 2009-2010 (2 years). – Abstract: design and develop visual analytical tools to identifying the causal relationships in government policies and domestic conflicts. DHS International Program: – Title: Deriving and Applying Cognitive Principles for Human/Computer Approaches to Complex Analytical Problems. PI: William Ribarsky, Co-PIs: Brian Fisher, Remco Chang, John Dill. $200,000. 2009-2010 (1 year). – Abstract: identifying new evaluation methods for visual analytical systems, and applying computational methods for analyzing user interactions. Quantitative Analysis Division at Bank of America – Exploration and analysis of financial risk

31. ComputingIntroVAGraphicsInteractionWrap-up 31/33 Future Work (On-going Collaborations) With NSF FODAVA Center at Georgia Tech (Dr. Haesun Park, director) – Interpreting user interactions to affecting machine learning algorithms – Visual PCA: using perceptual metrics to finding principle components – Applying perceptual constraint to dimension reduction: for animating temporal data in dimension reduction, find methods to maintain hysteresis With University of Kentucky (Drs. Judy Goldsmith, Jinze Liu, Phillip Chang, MD) – Integrating data mining (KDD), POMDP, and visual analytics to prevent sepsis by identifying biomarkers (Proposal in submission to NSF CDI) With geographer and architect at UNC Charlotte (Dr. Jean-Claude Thill and Eric Sauda) – Designing computational methods for identifying neighborhood characteristics (Proposal in submission to NSF IIS) – Applying the UrbanVis system to analyzing crime (proposal in preparation for DOJ/NIJ) With Virginia Tech (Dr. Chris North) and Pacific Northwest National Lab (Dr. Bill Pike and Richard May) – Developing a research agenda for analytic provenance (Workshop proposal in submission to DHS)

32. ComputingIntroVAGraphicsInteractionWrap-up 32/33 Thank you! Graphics & Visualization Computing Interaction & Reasoning rchang@uncc.edu http://www.viscenter.uncc.edu/~rchang

33. ComputingIntroVAGraphicsInteractionWrap-up 33/33 Acknowledgement Bill RibarskyZach Wartell Dong Hyun Jeong, Tom Butkiewicz, Xiaoyu Wang, Wenwen Dou, Tera Green From the Data Visualization Group (DVG) at UNC Charlotte

34. ComputingIntroVAGraphicsInteractionWrap-up 34/33 Acknowledgement Eric SaudaJean-Claude Thill From the Urban Visualization Group at UNC Charlotte Ginette WesselElizabeth Unruh

35. ComputingIntroVAGraphicsInteractionWrap-up 35/33 Acknowledgement More Collaborators… Nancy Pollard (CMU), Evan Suma (UNCC), Heather Lipford (UNCC), Dan Keefe (UMN), Caroline Ziemkiewicz (UNCC), Robert Kosara (UNCC), Mohammad Ghoniem Clockwise, starting on the left:

36. ComputingIntroVAGraphicsInteractionWrap-up 36/33 Acknowledgement And many many others… Joseph Kielman (DHS), Bill Pike (PNNL), Theresa O'Connell (NIST), Seok-Won Lee (UNCC), Brian Fisher (Simon Fraser), Alvin Lee (BofA), Jing Yang (UNCC), Daniel Kern (BofA), Agust Sudjianto (BofA), Erin Miller (UMD), Kathleen Smarick (UMD), Felesia Stukes (UNCC), Marcus Ewert (UMN), Larry Hodges (Clemson), Michael Butkiewicz (UC Riverside), Josh Jones (BofA), Alex Godwin (Charles River Analytics), Edd Hauser (UNCC), Shenen Chen (UNCC), Bill Tolone (UNCC), Wanqiu Liu (UNCC), Rashna Vatcha (UNCC)

37. ComputingIntroVAGraphicsInteractionWrap-up 37/33 Final Thought… “The sexy job in the next 10 years will be statisticians,” said Hal Varian, chief economist at Google. “And I’m not kidding.” Yet data is merely the raw material of knowledge. “We’re rapidly entering a world where everything can be monitored and measured,” said Erik Brynjolfsson, an economist and director of the Massachusetts Institute of Technology’s Center for Digital Business. “But the big problem is going to be the ability of humans to use, analyze and make sense of the data.” “The key is to let computers do what they are good at, which is trawling these massive data sets for something that is mathematically odd,” said Daniel Gruhl, an I.B.M. researcher whose recent work includes mining medical data to improve treatment. “And that makes it easier for humans to do what they are good at — explain those anomalies.” 1 1. New York Times. “For Today’s Graduate, Just One Word: Statistics “, August 5, 2009. Graphics & Visualization Computing Interaction & Reasoning

38. ComputingIntroVAGraphicsInteractionWrap-up 38/33 Backup Slides – Visual Analytics

39. ComputingIntroVAGraphicsInteractionWrap-up 39/33 Individually Not Unique Analytical Reasoning and Interaction Visual Representation Production, Presentation Dissemination Data Representation Transformation Validation and Evaluation Data Mining Machine Learning Databases Information Retrieval etc Tech Transfer Report Generation etc Quality Assurance User studies (HCI) etc Interaction Design Cognitive Psychology Intelligence Analysis etc. InfoVis SciVis Graphics etc

40. ComputingIntroVAGraphicsInteractionWrap-up 40/33 In Combinations of 2 or 3… Analytical Reasoning and Interaction Visual Representation Production, Presentation Dissemination Data Representation Transformation Validation and Evaluation Data Mining Machine Learning Databases Information Retrieval etc InfoVis SciVis Graphics etc

41. ComputingIntroVAGraphicsInteractionWrap-up 41/33 In Combinations of 2 or 3… Analytical Reasoning and Interaction Visual Representation Production, Presentation Dissemination Data Representation Transformation Validation and Evaluation Interaction Design Cognitive Psychology Intelligence Analysis etc. Tech Transfer Report Generation etc

42. ComputingIntroVAGraphicsInteractionWrap-up 42/33 This Talk Focuses On… Analytical Reasoning and Interaction Visual Representation Production, Presentation Dissemination Data Representation Transformation Validation and Evaluation Interaction Design Cognitive Psychology Intelligence Analysis etc. InfoVis SciVis Graphics etc Data Mining Machine Learning Databases Information Retrieval etc

43. ComputingIntroVAGraphicsInteractionWrap-up 43/33 Eureka: Visual Analytics!! “Saunders, perhaps you’re getting a bit carried away with the visual analytics!” 1 1: Slide courtesy of Dr. Maria Zemankova, NSF

44. ComputingIntroVAGraphicsInteractionWrap-up 44/33 Case Study on WireVis Analytical Reasoning and Interaction Visual Represent ation Production, Presentatio n Disseminati on Data Representat ion Transformat ion Validation and Evaluation User Centric – Designed system based on domain expertise Visual Interface – Multiple coordinated views that link multiple dimensions Interactive – Overview, drill-down, reclustering Data Clustering – Clustering by accounts, and search by example Production – Connected to a live database and beta-deployed at BofA (Validation) – Expert evaluation

45. ComputingIntroVAGraphicsInteractionWrap-up 45/33 Backup Slides – Urban Simplification

46. ComputingIntroVAGraphicsInteractionWrap-up 46/33 Algorithm to Preserve Legibility Identify and preserve Paths and Edges Create logical Districts and Nodes Simplify model while preserving Paths, Edges, Districts, and Nodes Hierarchically apply appropriate amount of texture Highlight Landmarks and choose models to render

47. ComputingIntroVAGraphicsInteractionWrap-up 47/33 Identifying and Preserving Paths and Edges

48. ComputingIntroVAGraphicsInteractionWrap-up 48/33 Single-Link Clustering – Iteratively groups the “closest” clusters together based on Euclidean distance – produces a binary tree (dendrogram) – Penalizes large clusters to create a more balanced tree Identifying and Preserving Paths and Edges (1) abcdef bcde def bcdef abcdef abc

49. ComputingIntroVAGraphicsInteractionWrap-up 49/33 Identifying and Preserving Paths and Edges (2)

50. ComputingIntroVAGraphicsInteractionWrap-up 50/33 Creating logical Districts and Nodes

51. ComputingIntroVAGraphicsInteractionWrap-up 51/33 Creating logical Districts and Nodes (1)

52. ComputingIntroVAGraphicsInteractionWrap-up 52/33 Merge two clusters by combining footprints Creating logical Districts and Nodes (2) (c) The resulting “Merged Hull” (d) The Introduced Error, or “Negative Space” (a)(b)(c)(d)

53. ComputingIntroVAGraphicsInteractionWrap-up 53/33 Simplification while preserving Paths, Edges, Nodes, and Districts

54. ComputingIntroVAGraphicsInteractionWrap-up 54/33 Simplification while preserving Paths, Edges, Nodes, and Districts (1) 6000 edges1000 edges Demo!

55. ComputingIntroVAGraphicsInteractionWrap-up 55/33 Simplification while preserving Paths, Edges, Nodes, and Districts (2) After the polylines have been simplified – Create “Cluster Meshes” – The height of the Cluster Mesh is the median height of all buildings in the cluster

56. ComputingIntroVAGraphicsInteractionWrap-up 56/33 Hierarchical Textures

57. ComputingIntroVAGraphicsInteractionWrap-up 57/33 Each Cluster Mesh contains 6 textures – 1 Side Texture – 1 top-down view of the roof texture – 4 roof textures from 4 angles (south, west, east, north) Hierarchical Textures (1) Side texture Top-downSouthWestEastNorth

58. ComputingIntroVAGraphicsInteractionWrap-up 58/33 Clusters are divided into “bins” based on their visual importance Each bin contains a texture atlas Texture atlases from all bins have the same dimension Hierarchical Textures (2) n/2n/4n/8 …. …

59. ComputingIntroVAGraphicsInteractionWrap-up 59/33 Runtime Levels of Detail

60. ComputingIntroVAGraphicsInteractionWrap-up 60/33 Starting with the root node of the dendrogram – Approximate the “Negative Space” as a 3D box – shown as the red box – Project the visible sides of the box onto screen space – Reject if the number of pixel is above a user-defined tolerance Runtime Levels of Detail abcdef bcde def abcdef abc

61. ComputingIntroVAGraphicsInteractionWrap-up 61/33 Landmark and Skyline Preservation (1) Original SkylineWith Landmark Preservation Without Landmark Preservation

62. ComputingIntroVAGraphicsInteractionWrap-up 62/33 – Project a user-defined pixel tolerance (α) onto the top of each cluster – If any building within that cluster is taller than the projected tolerance (shown in green), it is drawn separately from the cluster mesh. Landmark and Skyline Preservation (2)

63. ComputingIntroVAGraphicsInteractionWrap-up 63/33 Results

64. ComputingIntroVAGraphicsInteractionWrap-up 64/33 Backup Slides – VA Systems

65. ComputingIntroVAGraphicsInteractionWrap-up 65/33 (2) Investigative GTD Where When Who What Original Data Evidence Box R. Chang et al., Investigative Visual Analysis of Global Terrorism, Journal of Computer Graphics Forum (Eurovis), 2008.

66. ComputingIntroVAGraphicsInteractionWrap-up 66/33 WHY ? WHY ? This group’s attacks are not bounded by geo-locations but instead, religious beliefs. Its attack patterns changed with its developments. (2) Investigative GTD: Revealing Global Strategy

67. ComputingIntroVAGraphicsInteractionWrap-up 67/33 Domestic Group A geographically- bounded entity in the Philippines. The ThemeRiver shows its rise and fall as an entity and its modus operandi. (2) Investigative GTD: Discovering Unexpected Temporal Pattern

68. ComputingIntroVAGraphicsInteractionWrap-up 68/33 (3) Analysis of Biomechanical Motion Biomechanical motion sequences (animation) are difficult to analyze. Watching the movie repeatedly does not easily lead to insight. Collaboration with Brown University and Univ. of Minnesota to examine the mechanics of a pig chewing different types and amounts of food (nuts, pig chow, etc.) The data is typically organized by the rigid bodies in the model, where each rigid body contains 6 variables per frame -- 3 for translation, and 3 for rotation.

69. ComputingIntroVAGraphicsInteractionWrap-up 69/33 (3) Analysis of Biomechanical Motion R. Chang et al., Interactive Coordinated Multiple-View Visualization of Biomechanical Motion Data, IEEE Vis (TVCG) 2009. To Appear.

70. ComputingIntroVAGraphicsInteractionWrap-up 70/33 Our emphasis is on “interactive comparison.” Following the work by Robertson [InfoVis 2008], comparisons can be performed using: – Small Multiples – Side by side comparison – Overlap Between two datasets Different cycles in the same data (3) Analysis of Biomechanical Motion

71. ComputingIntroVAGraphicsInteractionWrap-up 71/33 Human + Computer: Dimension Reduction – Lost in Translation Biomechanical motion analysis revisited… – 6 degrees of freedom (x, y, z rotation and x, y, z translation) – One single joint Applying a non-linear dimension reduction method – Isomap – MDS embedding We found: – 3 latent dimensions – 2 of which are ambiguous…

72. ComputingIntroVAGraphicsInteractionWrap-up 72/33 What is in a User’s Interactions? Types of Human-Visualization Interactions – Word editing (input heavy, little output) – Browsing, watching a movie (output heavy, little input) – Visual Analysis (closer to 50-50) VisualizationHuman Output Input Keyboard, Mouse, etc Images (monitor)

73. ComputingIntroVAGraphicsInteractionWrap-up 73/33 What’s in a User’s Interactions Why are these so much lower than others? – (recovering “methods” at about 15%) Only capturing a user’s interaction in this case is insufficient.

74. ComputingIntroVAGraphicsInteractionWrap-up 74/33 Discussion What interactivity is not good for: – Presentation – YMMV = “your mileage may vary” Reproducibility: Users behave differently each time. Evaluation is difficult due to opportunistic discoveries.. – Often sacrifices accuracy iPCA – SVD takes time on large datasets, use iterative approximation algorithms such as onlineSVD. WireVis – Clustering of large datasets is slow. Either pre-compute or use more trivial “binning” methods.

75. ComputingIntroVAGraphicsInteractionWrap-up 75/33 Discussion Interestingly, – It doesn’t save you time… – And it doesn’t make a user more accurate in performing a task. However, there are empirical evidence that using interactivity: – Users are more engaged (don’t give up) – Users prefer these systems over static (query-based) systems – Users have a faster learning curve We need better measurements to determine the “benefits of interactivity”

76. ComputingIntroVAGraphicsInteractionWrap-up 76/33 Human + Computer: User Interactions – Lessons Learned Showing reasoning and intent are capturable. – Although the study is limited in scope, it establishes a foundation for interaction-capturing related research With interaction capturing, we might be able to collect all the thinking of expert analysts and create a knowledge base that is useful for – Training: many domain specific analytics tasks are difficult to teach – Guidance: use existing knowledge to guide future analyses – Verification, and validation: check to see if everything was done right. Automating the process of extracting thinking is the key. – By formulating user interactions as high dimensional vectors, we can apply analytical methods

77. ComputingIntroVAGraphicsInteractionWrap-up 77/33 Backup Slides – Professional Activities

78. ComputingIntroVAGraphicsInteractionWrap-up 78/33 Professional Activities Committee / Panelists – Program Committee: IEEE Conference on Visual Analytics, 2010 – Program Committee: SIG CHI Workshop on BELIV, 2010 – Program Committee: AAAI Spring-09 Symposium on Technosocial Predictive Analytics, 2009 – Panelist: 3rd Annual DHS University Summit. Panel: Research to Reality, 2009 – Panelist: 3rd Annual DHS University Summit. Panel: Visual Analytics and Discrete Science Integration into the DHS Center of Excellence Program, 2009 Invited Talks – Dec 13, 2006 Google Inc. Simplification of Urban Models based on Urban Legibility – July 6, 2007 Naval Research Lab. Urban Visualization – Oct 4, 2007 Charlotte Viscenter. Urban Visualization – Oct 17, 2007 Charlotte Metropolitan GIS Users Group. GIS and Urban Visualization – Nov 19, 2007 START Center at University of Maryland. Integrated Visual Analysis of the Global Terrorism Database – Nov 29, 2007 Charlotte Viscenter. Integrated Visual Analysis of the Global Terrorism Database – Jan 25, 2008 DoD/DHS Social Science Modeling and Information Visualization Symposium. Social Science and Information Visualization on Terrorism and Multimedia – May 14, 2008 Charlotte Metropolitan GIS User Group. Multi-Focused Geospatial Analysis Using Probes – Aug 27, 2008 DoD/DHS Symposium for Overcoming the Information Challenge in Federated Analysis: From Concept to Practice. Roadmap of Visualization – Mar 19, 2009 DHS University Summit. Panel: Research to Reality – Mar 19, 2009 DHS University Summit. Panel: Visual Analytics and Discrete Science Integration into the DHS Center of Excellence Program – Apr 27, 2009 University of Kentucky. Thinking Interactively with Visualization – May 29, 2009 University of Victoria. Thinking Interactively with Visualization – Jul 28, 2009 Pacific Northwest National Lab. Thinking Interactively with Visualization – Jul 30, 2009 Microsoft Research. Thinking Interactively with Visualization – Aug 19, 2009 National Visual Analytic Consortium. What Are Your Interactions Doing For Your Visualization? – Sep 30, 2009 University of Kentucky (Grand Rounds at the Department of Surgery). Preventing – Sepsis: Artificial Intelligence, Knowledge Discovery, and Visualization – Jan 21, 2010 Charlotte Viscenter. UrbanVis Research Group: Urban Analytics – Feb 25, 2010 University of Georgia (AI Institute). Thinking Interactively with Visualization

79. ComputingIntroVAGraphicsInteractionWrap-up 79/33 Summary of Contributions Contributions – Graphics/Visualization Urban modeling and visualization – Visualization + Interaction Role of interactivity in visual thinking Applying principles to real-world problems such as financial analytics, terrorism studies, bridge management, biomechanical motion analysis, etc. – Interaction + Computing Exploring principle component analysis Study of user interactions in visual analytics systems In particular, foundations in computer graphics help the development of a human + visual computing research agenda

80. ComputingIntroVAGraphicsInteractionWrap-up 80/33 Journal Publications (16) Urban Visualization – R. Chang, T. Butkiewicz, C. Ziemkiewicz, Z. Wartell, N. Pollard, and W. Ribarsky. Legible simplification of textured urban models. IEEE Computer Graphics and Applications, 28(3):27–36, 2008. – T. Butkiewicz, R. Chang, Z. Wartell, and W. Ribarsky. Visual analysis of urban change. Computer Graphics Forum, 27(3):903–910, 2008. – T. Butkiewicz, R. Chang, W. Ribarsky, and Z. Wartell. Understanding Dynamics of Geographic Domains, chapter Visual Analysis of Urban Terrain Dynamics, pages 151– 169. CRC Press/Taylor and Francis, 2007. – R. Chang, G. Wessel, R. Kosara, E. Sauda, and W. Ribarsky. Legible cities: Focus-dependent multi-resolution visualization of urban relationships. Visualization and Computer Graphics, IEEE Transactions on, 13(6):1169–1175, Nov.-Dec. 2007. Visualization and Visual Analytics – X. Wang, W. Dou, S.E. Chen, W. Ribarsky, and R. Chang. An interactive visual analytics system for bridge management. Computer Graphics Forum (Eurovis 2010), 2010. Conditional acceptance. – D. Keefe, M. Ewert, W. Ribarsky, and R. Chang. Interactive coordinated multiple-view visualization of biomechanical motion data. Visualization and Computer Graphics, IEEE Transactions on (IEEE Visualization Conference), 15(6):1383–1390, 2009 – X. Wang, D.H. Jeong, W. Dou, S.W. Lee, W. Ribarsky, and R. Chang. Defining and applying knowledge conversion processes to a visual analytics system. Computers & Graphics, July 2009. [Online] doi:10.1016/j.cag.2009.06.004 – D.H. Jeong, C. Ziemkiewicz, B. Fisher, W. Ribarsky, and R. Chang. iPCA: An interactive system for PCA-based visual analytics. Computer Graphics Forum, 28(3):767–774, 2009. – R. Chang, C. Ziemkiewicz, T.M. Green, and W. Ribarsky. Defining insight for visual analytics. IEEE Computer Graphics and Applications, 29(2):14–17, 2009. – R. Chang, A. Lee, M. Ghoniem, R. Kosara, W. Ribarsky, J. Yang, E. Suma, C. Ziemkiewicz, D. Kern, and A. Sudjianto. Scalable and interactive visual analysis of financial wire transactions for fraud detection. Information Visualization, 7:63–76(14), 2008. – X. Wang, E. Miller, K. Smarick, W. Ribarsky, and R. Chang. Investigative visual analysis of global terrorism database. Computer Graphics Forum, 27(3):919–926, 2008. Interaction & Provenance – W. Pike, J. Stasko, R. Chang, and T. O’Connell. Science of interaction. Information Visualization, 8:263–274, 2009. – W. Dou, D.H. Jeong, F. Stukes, W. Ribarsky, H. Lipford, and R. Chang. Recovering reasoning process from user interactions. IEEE Computer Graphics and Applications, 29(3):52–61, 2009 VR & Interface Designs – T. Butkiewicz, R. Chang, Z. Wartell, and W. Ribarsky. Alleviating the modifiable areal unit problem with probe-based geospatial analyses. Computer Graphics Forum (Eurovis 2010), 2010. Conditional acceptance – T. Butkiewicz, W. Dou, Z. Wartell, W. Ribarsky, and R. Chang. Multi-focused geospatial analysis using probes. Visualization and Computer Graphics, IEEE Transactions on, 14(6):1165–1172, Nov.-Dec. 2008. – D.H. Jeong, C. Song, R. Chang, and L. Hodges. User experimentation: An evaluation of velocity control techniques in immersive virtual environments. Springer-Verlag Virtual Reality, 13(1):41–50, Mar. 2009.

81. ComputingIntroVAGraphicsInteractionWrap-up 81/33 Conference/Workshop (22) R. Chang, C. Ziemkiewicz, R. Pyzh, J. Kielman, and W. Ribarsky. Learning-based evaluation of visual analytics systems. In ACM SIGCHI BELIV Workshop, 2010. Conditional acceptance. D. H. Jeong, T. Green, W. Ribarsky, and R. Chang. Comparative evaluation of two interface tools in performing visual analytics tasks. In ACM SIGCHI BELIV Workshop, 2010. Conditional acceptance. G. Wessel, E. Unruh, R. Chang, and E. Sauda. Urban user interface: Urban legibility reconsidered. In Southwest ACSA, 2010. D. H. Jeong, W. Dou, W. Ribarsky, and R. Chang. Knowledge-oriented refactoring in visualization. In IEEE Visualization Workshop on Refactoring Visualization From Experience, 2009. D. H. Jeong, W. Ribarsky, and R. Chang. Designing a PCA-based collaborative visual analytics system. In IEEE Visualization Workshop on Collaborative Visualization, 2009. W. Dou, D. H. Jeong, F. Stukes, W. Ribarsky, H. Lipford, and R. Chang. Comparing usage patterns of domain experts and novices in visual analytical tasks. In ACM SIGCHI Sensemaking Workshop 2009. X. Wang, W. Dou, R. Vatcha, W. Liu, S. E. Chen, S. W. Lee, R. Chang, and W. Ribarsky. Knowledge integrated visual analysis of bridge safety and maintenance. In SPIE 2009. X. Wang, W. Dou, W. Ribarsky, and R. Chang. Integration of heterogeneous processes through visual analytics. In SPIE 2009,. M. Butkiewicz, T. Butkiewicz, W. Ribarsky, and R. Chang. Integrating timeseries visualizations within parallel coordinates for exploratory analysis of incident databases. SPIE 2009. T. Butkiewicz, D. H. Jeong, W. Ribarsky, and R. Chang. Hierarchical multitouch selection techniques for collaborative geospatial analysis. In SPIE Defense, Security and Sensing 2009. D. H. Jeong, R. Chang, and W. Ribarsky. An alternative definition and model for knowledge visualization. In IEEE Visualization Workshop on Knowledge Assisted Visualization, 2008. X. Wang, W. Dou, S. W. Lee, W. Ribarsky, and R. Chang. Integrating visual analysis with ontological knowledge structure. In IEEE Workshop on Knowledge Assisted Visualization, 2008. D. H. Jeong, W. Dou, F. Stukes, W. Ribarsky, H. Lipford, and R. Chang. Evaluating the relationship between user interaction and financial visual analysis. In Visual Analytics Science and Technology. IEEE Symposium on, 2008. G. Wessel, R. Chang, and E. Sauda. Towards a new (mapping of the) city: Interactive, data rich modes of urban legibility. In Association for Computer Aided Design in Architecture, 2008. G. Wessel, R. Chang, and E. Sauda. Visualizing GIS: Urban form and data structure. Seeking the City: Visionaries on the Margins, ACSA, 2008. G. Wessel, E. Sauda, and R. Chang. Urban visualization: Urban design and computer visualization. In CAADRIA 2008. T. Butkiewicz, R. Chang, Z. Wartell, and W. Ribarsky. Visual analysis for live lidar battlefield change detection. SPIE, 2008. J. Jones, R. Chang, T. Butkiewicz, and W. Ribarsky. Visualizing uncertainty for geographical information in the global terrorism database. SPIE, 2008. A. Godwin, R. Chang, R. Kosara, and W. Ribarsky. Visual analysis of entity relationships in the global terrorism database. SPIE, 2008. T. Butkiewicz, R. Chang, Z. Wartell, and W. Ribarsky. Analyzing sampled terrain volumetrically with regard to error and geologic variation. SPIE, 2007. R. Chang, M. Ghoniem, R. Kosara, W. Ribarsky, J. Yang, E. Suma, C. Ziemkiewicz, D. Kern, and A. Sudjianto. Wirevis: Visualization of categorical, time-varying data from financial transactions. In Visual Analytics Science and Technology, 2007, IEEE Symposium on, 2007. R. Chang, T. Butkiewicz, C. Ziemkiewicz, Z. Wartell, N. Pollard, and W. Ribarsky. Hierarchical simplification of city models to maintain urban legibility. In SIGGRAPH ’06: ACM SIGGRAPH 2006 Sketches, 2006. R. Chang, R. Kosara, A. Godwin, and W. Ribarsky. Towards a role of visualization in social modeling. AAAI 2009 Spring Symposium on Technosocial Predictive Analytics, 2009. G. Wessel, E. Sauda, and R. Chang. Mapping understanding:Transforming topographic maps into cognitive maps. GeoVis Hamburg Workshop, 2009.