Content Motivation & Contextualization Definitions Concepts Models in Visualization From the past … Different levels … Theory of Visualization Process: Conceptual Models Survey ? Overview ? Knowledge-Assisted Visualization Challenges and Opportunities ? Guidance Challenges & Opportunities Silvia Miksch & Team Conclusion January 22, 2018 Defining Visualization Definition: Visualization Process [Munzner, 2014] "The use of computer-supported, interactive, visual representations of data to amplify cognition.” Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively. [Card et al., 1999] Why?... “The purpose of computing is insight, not numbers.” [Hamming, 1962] "The purpose of visualization is insight, not pictures." Visualization dates as an organized subfield from the NSF report, Visualization in Scientific Computing [McCormick and DeFanti, 1987].
Why have a human in the loop? Why use an external representation? [Munzner, 2014] [Munzner, 2014] Computer-based visualization systems provide visual representations of datasets Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively. designed to help people carry out tasks more effectively. Visualization is suitable when there is a need to augment human capabilities external representation: replace cognition with perception rather than replace people with computational decision-making methods. don’t need vis when fully automatic solution exists and is trusted many analysis problems ill‐specified don’t know exactly what questions to ask in advance possibilities long‐term use for end users (e.g., exploratory analysis of scientific data) presentation of known results stepping stone to better understanding of requirements before developing models help developers of automatic solution refine/debug, determine parameters [Cerebral: Visualizing Multiple Experimental Conditions on a Graph with Biological Context. Barsky, Munzner, Gardy, help end users of automatic solutions verify, build trust and Kincaid. IEEE TVCG (Proc. InfoVis) 14(6):1253-1260, 2008.] Why represent all the data? Purpose/Goals ::: Visualization [Munzner, 2014] Presentation (Communication) Computer-based visualization systems provide visual representations of datasets Starting point : facts to be presented are fixed a priori interactivity designed to help people carry out tasks more effectively. Process: choice of appropriate presentation techniques summaries lose information, details matter Result: high-quality visualization of the data to present facts confirm expected and find unexpected patterns Confirmatory Analysis assess validity of statistical model Starting point: hypotheses about the data Process: goal-oriented examination of the hypotheses Anscombe’s Quartet Result: visualization of data to confirm or reject the hypotheses Identical statistics Exploratory Analysis x mean 9 x variance 10 Starting point: no hypotheses about the data y mean 8 Process: interactive, usually undirected search for structures, trends y variance 4 Result: visualization of data to lead to hypotheses about the data x/y correlation 1
[Aigner, Miksch, Schumann, Tominski, 2011] 3 Key Questions of the Visualization [Munzner 2014] 1. What has to be presented? Visualization Analysis & Design – Time and data! Tamara Munzner Department of Computer Science 2. Why has it to be presented? University of British Columbia – User tasks! 3. How is it presented? – Visual representation! [Munzner 2014] Analysis framework: 4 levels, 3 questions [Munzner 2014] domain abstraction domain situation idiom algorithm who are the target users? [A Nested Model of Visualization Design and Validation. abstraction Munzner. IEEE TVCG 15(6):921-928, 2009 (Proc. InfoVis 2009). ] translate from specifics of domain to vocabulary of vis what is shown? data abstraction often don’t just draw what you’re given: transform to new form why is the user looking at it? task abstraction domain idiom abstraction how is it shown? visual encoding idiom : how to draw idiom interaction idiom : how to manipulate algorithm algorithm efficient computation [A Multi-Level Typology of Abstract Visualization Tasks Brehmer and Munzner. IEEE TVCG 19(12):2376-2385, 2013 (Proc. InfoVis 2013). ]
[Munzner 2014] [Munzner 2014] Content Jock Mackinlay 1986 Motivation & Contextualization Definitions Concepts Models in Visualization From the past … Different levels … Conceptual Models Knowledge-Assisted Visualization Guidance formal specification language that combines query, analysis, and visualization Challenges & Opportunities into a single framework automatic visual representation of data translated Bertin’s semiological texts Conclusion into a useful piece of software (and badly-needed visualization theory)
Knowledge Crystallization Loop [Pirolli & [Card, et al. 1999] Card, 2005] Sub-tasks Overview Task Zoom Create, Filter Forage Extract Details Decide, for Data Compose Browse or Act Present Search query Reorder Search for Problem- Create Cluster Schema Solve Delete Class Manipulate Average Instantiated Read fact Promote Read pattern Schema Detect pattern Read compare Abstract taken from [Thomas & Cook 2005] [Card, et al., 1999] Visualization Reference Model Data Flow Model & Data State Model [Chi 2000] Data Visual Form Task Raw Data Visual Views Data Tables Structures Visual Data View Transformations Mappings Transformations Human Interaction (controls) Data Transformations Mapping raw data into an organization fit for visualization Visual Mappings Encoding abstract data into a visual representation View Transformations Changing the view or perspective onto the visual representation User interaction can feed back into any level [Card, Mackinlay, & Shneiderman, 1999]
The Value of Visualization (Operational Model) Cognitive Conversation Process Model [van Wijk, 2005] [Wang, et al., 2009] Event-Based Visualization Visual Analytics – Process [Tomiski, 2011] [Keim, et al., 2008]
Knowledge Generation Model for VA Knowledge Generation Model for VA [Sacha, et al., 2014] [Sacha, et al., 2014] Domain Knowledge in the VA Process User‐Centered Design [Lammarsch et al., 2011] data Visual Analytics Methods users/audience goals/tasks appropriateness
Content Motivation & Contextualization Definitions Concepts Models in Visualization From the past … The role of explicit Different levels … knowledge: 1 Conceptual Models a conceptual model of Knowledge-Assisted Visualization knowledge-assisted visual analytics Guidance Paolo Federico 1 , Markus Wagner 12 , 2 Alexander Rind 12 , Albert Amor-Amorós 1 , Challenges & Opportunities Silvia Miksch 1 , Wolfgang Aigner 12 Conclusion Knowledge in Visualization wisdom tacit knowledge-assisted knowledge visualization Explicit Knowledge = “Data that represents information explicit data the results of a computer-simulated cognitive process, [Chen M. et al., 2009] [Wang, 2009] such as perception, learning, association, and reasoning, [Ackoff, 1989] or the transcripts of some knowledge acquired by human beings” [Chen et al., 2009] prior domain knowledge knowledge-based in the KDD interfaces operational process [Pike et al., 2009] [Chen C., 2005] [Fayyad et al., 1996]
All processes Knowledge processes data Generation Transformation specification Visualization Internalization tacit knowledge Knowledge Visualization Analysis explicit knowledge Simulation image Exploitation visualization Externalization Visualization perception Direct Externalization Intelligent Analysis Interaction Mining analysis Guidance externalization exploration Characterizing Analysis Characterizing Knowledge Type Origin Space Operational Pre-design Cognitive/Perceptual Domain/Declarative Design Computational M mining U Domain/Procedural Data simulation G Single User guidance Multiple Users
[Ceneda, et al., 2017] Gnaeus Characterizing Guidance in Visual Analysis Processes Data Visualization Knowledge Visualization Simulation Intelligent Data Analysis Guidance Type Domain, Declarative Davide Ceneda, Theresia Gschwandtner, Silvia Miksch Domain, Procedural Hans-Jörg Schulz, Christian Tominski Origin Thorsten May Pre-design Marc Streit [Federico et al., 2015] … by the number of visualizations… Many VA users are overwhelmed … … algorithms … Algorithms
… and possible parameters When trying out each and every option is not possible Algorithms [Ceneda, et al., 2017] What is Guidance in VA? We can support users with The Short Answer Guidance is a computer-assisted process that aims to actively resolve a knowledge gap encountered by users during an interactive VA session . Guidance Based on [Smith and Mosier 1986] [Engels 1996] [Dix et al. 2004] there
[Ceneda, et al., 2017] [Ceneda, et al., 2017] Characterizing Knowledge Gap & Process Aspects of Guidance 90+ Papers Towards a Characterization of Guidance in Visualization [Schulz et al. 2013] [Ceneda, et al., 2017] [Ceneda, et al., 2017] Aspects of Guidance Aspects of Guidance
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