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Intro Vis/Gfx Interaction Evaluation Wrap-up Thinking Interactively with Visualizations Remco Chang UNC Charlotte Charlotte Visualization Center Intro Vis/Gfx Interaction Evaluation Wrap-up Definition of Visual Analytics • Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces [Thomas & Cook 2005] • 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. Intro Vis/Gfx Interaction Evaluation Wrap-up Individually Not Unique • Data Mining • Machine Learning • Databases • Information Retrieval • etc Analytical Reasoning and Interaction Data Representation Transformation Production, Presentation Dissemination • Tech Transfer • Report Generation • etc • • • • Interaction Design Cognitive Psychology Intelligence Analysis etc. Visual Representation • • • • InfoVis SciVis Graphics etc Validation and Evaluation • Quality Assurance • User studies (HCI) • etc Intro Vis/Gfx Interaction Evaluation Wrap-up In Combinations of 2 or 3… • Data Mining • Machine Learning • Databases • Information Retrieval • etc Analytical Reasoning and Interaction Data Representation Transformation Production, Presentation Dissemination Visual Representation Validation and Evaluation • • • • InfoVis SciVis Graphics etc Intro Vis/Gfx Interaction Evaluation Wrap-up In Combinations of 2 or 3… Analytical Reasoning and Interaction Data Representation Transformation Production, Presentation Dissemination • Tech Transfer • Report Generation • etc • • • • Interaction Design Cognitive Psychology Intelligence Analysis etc. Visual Representation Validation and Evaluation Intro Vis/Gfx Interaction Evaluation Wrap-up This Talk Focuses On… Analytical Reasoning and Interaction Data Representation Transformation Production, Presentation Dissemination • • • • Interaction Design Cognitive Psychology Intelligence Analysis etc. Visual Representation • • • • InfoVis SciVis Graphics etc Validation and Evaluation • Quality Assurance • User studies (HCI) • etc Intro Vis/Gfx Interaction Evaluation Wrap-up Interactive Analysis + Visualization • Most people in the visualization community believe that interactivity is essential for visualization and visual analytics: – “A [visual] analysis session is more of a dialog between the analyst and the data… the manifestation of this dialog is the analyst’s interactions with the data representation” [Thomas & Cook 2005] – “Without interaction, [a visualization] technique or system becomes a static image or autonomously animated images” [Yi et al. 2007] • The goal of this talk is to consider the role of interaction in computer graphics, information visualization, and visual analytics. • First, we consider a stereotypical graphics application and try adding interaction to it.. Intro Vis/Gfx Interaction Evaluation Wrap-up 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. Intro Vis/Gfx Interaction Evaluation Wrap-up Urban Simplification • Which polygons to remove? Original Model Our Textured Model Simplified Model using QSlim Our Model Visually different, but quantitatively similar! Intro Vis/Gfx Interaction Evaluation Wrap-up 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 Intro Vis/Gfx Interaction Evaluation Wrap-up 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 there has a strong mix of ethnicities.” Geometric, Information, View Dependent (Cognitive) Intro Vis/Gfx Interaction Evaluation Wrap-up 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 Intro Vis/Gfx Interaction Evaluation Wrap-up The Role of Interaction in Visualization • We can use interactions to… [Yi et al. 2007] – – – – – – – Select: mark something as interesting Explore: show me something else Reconfigure: show me a different arrangement Encode: show me a different representation Abstract/Elaborate: show me more or less detail Filter: show me something conditionally Connect: show me related items • In other words, we can use interactions to think. Intro Vis/Gfx Interaction Evaluation Wrap-up (1) WireVis: Financial Fraud Analysis • In collaboration with Bank of America – Looks for suspicious wire transactions – Currently beta-deployed at WireWatch – Visualizes 15 million transactions over 1 year • Uses interaction to coordinate four perspectives: – – – – Keywords to Accounts Keywords to Keywords Keywords/Accounts over Time Account similarities (search by example) Intro Vis/Gfx Interaction Evaluation Wrap-up (1) WireVis: Financial Fraud Analysis Heatmap View (Accounts to Keywords Relationship) Search by Example (Find Similar Accounts) Keyword Network (Keyword Relationships) Strings and Beads (Relationships over Time) 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. Intro Vis/Gfx Interaction Evaluation Wrap-up (2) Investigative GTD • Collaboration with U. Maryland’s DHS Center of Excellence START (Study of Terrorism And Response to Terrorism) – Global Terrorism Database (GTD) – International terrorism activities from 1970-1997 – 60,000 incidents recorded over 120 dimensions • Visualization is designed to be “investigative” in that it is modeled after the 5 W’s: – Who, what, where, when, and [why] – Interaction allows the user to adjust one or more of the W’s and see how that affects the other W’s Intro Vis/Gfx Interaction Evaluation Wrap-up (2) Investigative GTD Who Where What Evidence Box Original Data R. Chang et al., Investigative Visual Analysis of Global Terrorism, Journal of Computer Graphics Forum (Eurovis), 2008. When Intro Vis/Gfx Interaction Evaluation Wrap-up (2) Investigative GTD: Revealing Global Strategy This group’s attacks are not bounded by geo-locations but instead, religious beliefs. Its attack patterns changed with its developments. Intro Vis/Gfx Interaction Evaluation Wrap-up (2) Investigative GTD: Discovering Unexpected Temporal Pattern A geographicallybounded entity in the Philippines. The ThemeRiver shows its rise and fall as an entity and its modus operandi. Domestic Group Intro Vis/Gfx Interaction Evaluation Wrap-up (3) iPCA: Interactive PCA • Quick Refresher of Principle Component Analysis (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”. Intro Vis/Gfx Interaction Evaluation Wrap-up (3) iPCA: Interactive PCA R. Chang et al., iPCA: An Interactive System for PCA-based Visual Analytics. Computer Graphics Forum (Eurovis), 2009. To Appear. Intro Vis/Gfx Interaction Evaluation Wrap-up (3) Evaluation – iPCA vs. SAS/INSIGHT • Results – A bit more accurate – People don’t “give up” – Not faster • Overall preference – Using letter grades (A through F) with “A” representing excellent and F a failing grade. Intro Vis/Gfx Interaction Evaluation Wrap-up If (Interactions == Thinking)… • What is in a user’s interactions? • If (interactions == thinking), what can we learn from the user’s interactions? • Is it possible to extract “thinking” from “interactions”? Intro Vis/Gfx Interaction Evaluation Wrap-up What is in a User’s Interactions? Keyboard, Mouse, etc Input Visualization Human Output Images (monitor) • 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) Intro Vis/Gfx Interaction Evaluation Wrap-up What is in a User’s Interactions? • Goal: determine if there really is “thinking” in a user’s interactions. Grad Students (Coders) Compare! (manually) Analysts Strategies Methods Findings Guesses of Analysts’ thinking Logged (semantic) Interactions WireVis Interaction-Log Vis Intro Vis/Gfx Interaction Evaluation Wrap-up 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. Intro Vis/Gfx Interaction Evaluation Wrap-up 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. Intro Vis/Gfx Interaction Evaluation Wrap-up Lessons Learned • We have proven that a great deal of an analyst’s “thinking” in using a visualization is capturable and extractable. • Using semantic interaction capturing, we might be able to collect all the thinking of expert analysts and create a knowledge database 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: go back and check to see if everything was done right. • But not all visualizations are interactive, and not all thinking is reflected in the interactions. – A model of how and what to capture in a visualization for extracting an analyst’s thinking process is necessary. Intro Vis/Gfx Interaction Evaluation Wrap-up Conclusion • Interactions are important for visualization and visual analysis – In considering interactions, one must be aware of the necessary speed and frame rate of the displays. • Techniques such as simplification, LOD, or approximation can be used. – Interactions have been proven to help the understanding of complex problems. • Relevant interactions have been integrated in multiple visualizations for different domains and demonstrated significant impact. – Capturing and storing analysts’ interactions have great potential • They can be aggregated to become a “knowledge database” that has traditionally been difficult to create manually. Intro Vis/Gfx Interaction Evaluation Wrap-up 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. Intro Vis/Gfx Interaction Evaluation 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” Wrap-up Intro Vis/Gfx Interaction Evaluation Wrap-up Future Work • Data Mining • Machine Learning • Databases • Information Retrieval • etc Analytical Reasoning and Interaction Data Representation Transformation Production, Presentation Dissemination • Tech Transfer • Report Generation • etc • • • • Interaction Design Cognitive Psychology Intelligence Analysis etc. Visual Representation • • • • InfoVis SciVis Graphics etc Validation and Evaluation • Quality Assurance • User studies (HCI) • etc Intro Vis/Gfx Interaction Evaluation Wrap-up Future Work • Lots of possible combinations. Are they all meaningful? • Of particular interest to me is “Data + Interaction + Visualization” – How to apply computational approaches to find solutions that are usable by humans? • Linear (PCA) and non-linear (manifold learning) create dimensions that are semantically difficult to define • Nodes within a Bayesian network are difficult to comprehend, therefore the results difficult to take at face value. Intro Vis/Gfx Interaction Evaluation Wrap-up Thank you! [email protected] http://www.viscenter.uncc.edu/~rchang