1. Taxonomies of Visualization Techniques CMPT 455/826 - Week 12, Day 2 w12d2 Sept-Dec 20091

2. A Framework for Visual Data Mining of Structures By Schulz, Nocke, and Schumann w12d2 Sept-Dec 20092

3. Design Criteria for a Visualization Toolset Generality –For different applications –For various users –Modular Flexibility –Flexible control mechanisms –Visual queries –Supports derived data Usability –Data abstraction –Acceptable reply times –Intuitive interface w12d2 Sept-Dec 20093

4. Tool components They describe a number of tools to: –Pre-process data before visualization –Allow the user to interact with the visualization –Provide an algorithmic kernel to develop various visualizations –Provide post-processing w12d2 Sept-Dec 20094

5. 5 Pre-processing User Interaction Post- processing and Interaction Algorithmic Kernel

6. A Knowledge Task-Based Framework for Design and Evaluation of Information Visualizations By Amar and Stasko w12d2 Sept-Dec 20096

7. Typical Problems Limited Affordances –The operations afforded by many visualization systems – are equivalent to very simple database queries. Predetermined Representations –The representations employed by common visualizations are not particularly agile, –supporting the formation of simplistic, static cognitive models –from elementary queries on typically historical, cross-sectional data. Decline Of Determinism In Decision-Making –We live in a world that is not only dominated by information, – but also by uncertainty. w12d2 Sept-Dec 20097

8. Bridging The Analytic Gaps: Knowledge Tasks The Use Of Taxonomies –the development of taxonomies for organizing low level tasks that a visualization should facilitate, and automatically creating presentations that match these tasks to appropriate techniques Rationale-Based Tasks –relate data sets to the realms in which decisions are being made Worldview-Based Tasks –indirectly support formulation of a strategy for browsing a visualization by providing insights as to what data should be explored to clarify certain relationships or test certain hypotheses. w12d2 Sept-Dec 20098

9. Knowledge tasks and scenarios Expose Uncertainty Concretize Relationships Formulate Cause And Effect Determine Domain Parameters Explain Multivariate Trends Confirm Hypotheses w12d2 Sept-Dec 20099

10. A Taxonomy of Tasks for Guiding the Evaluation of Multidimensional Visualizations By Valiati, Pimenta, and Freitas w12d2 Sept-Dec 200910

11. Operations for analyzing data Locate: the user knows a dataset entry and indicate it by pointing or describing it. Identify: similar to locate but the user describe the dataset entry without knowing it previously. Distinguish: different objects should be presented as distinct visual items. Categorize: objects may be different because they belong to different categories, which should be described by the user. Cluster: the system may find out categories and objects belonging to them are shown linked or grouped together. Distribution: the user specifies categories and objects belonging to them are distributed among them. Rank: the user is asked to indicate the order of the objects displayed. Compare: the user is asked to compare entities based on their attributes. Compare within and between relations: the user is asked to compare similar entities or different sets of objects. Associate: the user is asked to establish relations between objects displayed. Correlate: the user may observe shared attributes between objects Based on: Wehrend and Lewis w12d2 Sept-Dec 200911

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13. Rethinking Visualization: A High-Level Taxonomy By Tory and Möller w12d2 Sept-Dec 200913

14. Classification of visualization tasks w12d2 Sept-Dec 200914 The classification is broken down according to how much the spatialization is constrained and whether the design model is continuous or discrete (with or without structure). Colours match figure text to outlined / shaded areas