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Data & Information Visualization

Data & Information Visualization. Lecture 1: Data, Information, Knowledge and Their Presentations. Data & Information Visualization. Subject site: http://staff.it.uts.edu.au/~maolin/32146_DIV/. Data, Information, Knowledge.

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Data & Information Visualization

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  1. Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations

  2. Data & Information Visualization Subject site: http://staff.it.uts.edu.au/~maolin/32146_DIV/

  3. Data, Information, Knowledge • Data thing: a fundamental, indivisible thing in databases and data sets. • Can be represented naturally by populations and labels. • Associations between things. • If an association can be described by a succinct, computable rule it is called an explicit association. • If an association can not be described by a succinct, computable rule it is called an implicit association. • An information thing is an implicit association between the data things. • A knowledge thing is an explicit association between the data things or information things.

  4. Data, Information, Knowledge • Data: raw, uninterpreted facts Tom, 20 years old, student, turner • Information relates items of Data Tom is 20 years old • Knowledge relates items of Information Tom is 20 years old  Tom pays > $1, 500 Insurance • Modeling the world (Generalise) [18 − 25] years old  P (accident) = high

  5. Data mining  Knowledge discovery

  6. Knowledge Knowledge Visualization of the output Visualization of the output output output Data Mining Algorithms Data Mining Algorithms Visualization of the input input input Data Data

  7. Knowledge Visualization of the output output IntermediateVisualization Data Mining Algorithms Visualization of the input input Data

  8. Mapping attributes to visualisation Generation of visual models Model selection and validation Sourcedata Visualisationsystem Visualmodels Analyticaltechniques Integrateddatasets …. Model A • Decision trees • Association analysis • Rule induction • Clustering • Graph statistics …. Model B Reselection Remapping Regenerating ….

  9. Local URL .com .dk 24:00 Time Domains

  10. Visualization Scientific Visualization Information Visualization None Graph Visualization Graph Visualization Graph G = (V, E)

  11. The Definition of IV Information visualization: the use of interactive visual representations of abstract, non-physically based data to amplify cognition [CMS99]. [CMS99] Stuart K. Card, Jock D. Mackinlay, and Ben Shneiderman. Readings in information visualization: using vision to think. Morgan Kaufmann Publishers, Inc., 1999. Xerox Palo Alto Research Center (PARC)

  12. Reference Model Visualization: Mapping from data to visual form DATA VISUAL FORM Data Data Tables Visual Structures Views View Transformations Data Transformations Visual Mappings Human Interaction

  13. Data Tables • Relational descriptions of data extended to include metadata Analogy to database: Variable -> attribute; Case -> tuple or record

  14. Data Tables (2) • Variable Types • N = Nominal • Unordered set • O = Ordinal • Ordered set • Q = Quantitative • Numeric range • Metadata • Structure

  15. Data Transformations • Values  Derived Values • Structure  Derived Structure • Values  Derived Structure • Structure  Derived Values • Examples?

  16. Visual Structures • Data Tables are mapped to Visual Structures • Expressive, effective • Perception…and the human eye…

  17. Why do we need visual structures? Maps, diagrams, and PERT charts are examples of using visual representations to see things. A good picture is worth ten thousand words. Today, computers help people to see and understand abstract data through pictures.

  18. Visual Presentations of data None-relational data & Relational data An example of using SeeNet to view email data volumes generated by AT&T long distance network traffic. Edges represent email connections. Weigh and colors of edges represent volumes of email data. The little image dots represent data records of the number of sun spots, from 1850 to 1993, zoomed in on a small area. (collected from GVU Center, Georgia I. T.)

  19. Visual Structures (2) • Spatial substrates • Marks • Graphical properties

  20. Spatial Substrate • Space is the container unto which other parts of Visual Structure are poured. • Composition • Alignment • Folding • Recursion • Overloading

  21. Marks • Points • Lines • Areas • Volumes • Graphs and Trees – to show relations or links among objects

  22. Graph-Driven Visualization of Relational Data Graph Visualization An example of graph visualization. This is the visualization of a family tree (graph). Here each image node represents a person and the edges represent relationships among these people in a large family.

  23. Retinal Properties • Type of graphical property • Position/Size • Gray Scale • Orientation • Color • Texture • Shape

  24. Other Graphical Properties • Crispness • Resolution • Transparency • Arrangement • Color: value, hue, saturation • Table 1.22 • Finally, temporal encoding for visual structures

  25. Attributed Visualization Visualization of collaborative workspace

  26. View Transformations • Interactively modify and augment Visual Structures • Location Probes • Viewpoint Controls • Zoom, pan, clip • Overview an detail • Distortions • To perceive larger Visual Structure via distortion

  27. Human Interaction and Transformation • Direct Manipulation • Controlling Mappings

  28. Application1:Visual Web browser • WebOFDAV - mapping the entire Web, • Look at the whole of WWW as one graph; a huge and partially unknown graph. • Maintain and display a subset of this huge graph incrementally. • Reduce mouse-click rate • Maintain a 2D map & history of navigation

  29. The “lost in hyperspace” problem • Even in this small document, which could be read in one hour, users experienced the ‘lost in hyperspace’ phenomenon as exemplified by the following user comment: ‘ I soon realized that if I did not read something when I stumbled across it, then I would not be able to find it later.’ Of the respondents, 56% agreed fully or partly with the statement, ‘When reading the report, I was often confused about where I was.’ [Nielson, 1990].

  30. Visual Web Browser addresses the problem of “lost in hyperspace” with a sense of “space”. • Graphic Web Browser addresses the fundamental problem of “lost in hyperspace” by displaying a sequence of logical visual frames with a graphic “history tail” to track the user’s current location and keep records of his previous locations in the huge information space. • The logical neighborhood of the focus nodes indicates the current location of the user, and the tail of history indicates the path of the past locations during the navigation.

  31. Application2:File Managementand Site Mapping An example of using Space-Optimized Tree Visualization for a small web site mapping (approximately 80 pages) - viewing techniques needed Mapping to a Unix root with approx. 3700 directories and files

  32. Application3: Web Reverse Engineering • HWIT (Human Web Interface Tool) is able to reuse existing structures of web site by visualizing and modifying the corresponding web graphs, and then re-generating a new site by save the modified web graphs. The layout of an existing structure of a web site Enhancing the existing Web site by adding a sub-site

  33. Application4: B2C e-Commerce • VOS (Visual Online Shop) can be used for online grocery shopping, shopping cart model. It is applicable to any e-commerce shopping application (dynamically navigate e-catalogs).

  34. Application5: Online Business Process Management • WbIVC (Web-based Interactive Visual Component) is applied to a research project management system (RPMS) in universities. • A participant can review the details of a specific process element by clicking on the corresponding rectangle, and then selecting the “open a process element” in the popup menu. • A participant can also create a new artifact (a Java methods) to a research project by opening a edit window. The output interface of the WbIVC in RPMS The input interface of the WbIVC in RPMS

  35. Application6: Program Understandingand Software Mining • JavaMiner is for non-linear visual browsing of huge java code for programming understanding. • textual data mining • Visualize a variety of relationships between terms in Java code, e.g. HAS, SUBCLASS, CALL and INTERFACE relationships. • Text documents, the lexicon, the neighborhood function The input interface of the WbIVC in RPMS

  36. Conclusion • Reference model approximates the basic steps for visualizing information • Steps are an ongoing process with many iterations • Goal of information visualization: develop effective mappings to increase ability to think/to improve cognition

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