1. Multiscale Visualization Using Data Cubes Chris Stolte, Diane Tang, Pat Hanrahan Stanford University Information Visualization October 2002 Boston, MA

2. Motivation  Large multidimensional databases have become very common  Need techniques for exploration and analysis  “Overview first, zoom and filter, then details-on-demand”

3. Multiscale Visualization  Visual representation changes as user pans and zooms Overview, lots of data  highly abstracted Zoom, data density decreases  detailed information shown  Visual and data abstraction Visual abstraction different representation/same data Data abstraction transformations to reduce data set size

4. Existing Multiscale Visualizations  Cartography  Multiscale information visualization Pad++: alternate desktops DataSplash XmdvTool ADVIZOR  Main limitations: One zoom path Primarily visual abstraction

5. Contributions  Multiscale visualization with both visual and data abstraction using generalized mechanisms: Data Abstraction  Data Cubes Visual Abstraction  Polaris  “Design Patterns”

6. Data Cubes

7. Data Warehouses State Month Product Name Profit Sales Payroll Marketing Inventory Margin... Ordinal fields (categorical dimensions) Quantitative fields (measures) Fact table Coffee chain (courtesy Visual Insights)  Store data for analysis (OLAP)  Fact table contains measures categorized by dimensions:

8. Hierarchical Structure Location Market State Time Year Quarter Month Products Product Type Product Name Fact table Dimension tables State Month Product Name Profit Sales Payroll Marketing Inventory Margin...  Data warehouses are very large—need to summarize  Add hierarchical structure to warehouse

9. Hierarchical Dimensions Time Year Quarter Month  Each dimension table describes a tree  Each level describes a level-of-detail  Meaningful basis for aggregation  Create summaries of fact table for each level-of-detail as Data Cubes

10. Data Cube  Create cube for each level-of-detail combination  Summary of fact table Each cell aggregates all measures for those dimensions Each cube axis corresponds to a dimension in the relation at a level-of-detail Cube for (Quarter, Product Type, Market)

11. Hierarchies & Data Cubes  Hierarchies define a lattice of cubes: Each cube is defined by a level-of-detail in each dimension Least detailed Most detailed Data abstraction

12. Projecting Data Cubes  Can further abstract a cube by “projection” Data abstraction

13. Data Cube Summary  Industry standard for storing analytic data  Provide summaries of data at meaningful levels of detail  To perform data abstraction: Design a hierarchical schema Choose a cube in the lattice of cubes Project to relevant dimensions  Identifying a projection corresponds to specifying the desired data abstraction

14. Polaris

15. Exploring Data Cubes using Polaris  Polaris is: A UI for exploration, analysis of data warehouses A formal language for specifying queries & visualizations An interpreter for compiling specification into queries/drawing commands  Demo!

16. Polaris Formalism  Visualization described using visual specifications that define: Table configuration (algebra) Type of graphic in each pane Encoding of data as visual properties of marks (encoding system) Data transformations and queries  Each specification corresponds to a projection of the data cube

17. Path of Exploration  Can think of an analysis as path of specifications

18. Path of Exploration Visual abstraction

19. Path of Exploration Data abstraction This is a multiscale visualization!

20. Graphical Notation

21. Graphical Notation: Templates Instance Template

22. Specifying Multiscale Visualizations  Specify multiscale visualization using a graph of Polaris specifications: a Zoom Graph  Paper describes how to implement using Polaris  Polaris Specification  Possible zoom Zooming

23. Specifying Multiscale Visualizations  Can specify a zooming pattern by using templates

24. Specifying Multiscale Visualizations  Independent zooming on different dimensions is described as a graph x-axis zoom y-axis zoom

25. Design Patterns

26.  Zoom graphs simplify specifying and implementing multiscale visualizations Design is still very hard  “ Design patterns ” (a la Gamma et al.) Capture zoom structures that have been used effectively & reuse in new designs We present four such patterns Formal way to discuss multiscale visualization

27. Thematic Maps

31. Chart Stacks

35. Matrices

36. Matrices

37. Matrices

38. Matrices

39. Dependent QQ Plots

40. Summary

41. Summary  Multiscale visualization with both visual and data abstraction using generalized mechanisms: Data Abstraction  Data Cubes Visual Abstraction  Polaris  “Zoom Graphs” for specifying and implementing multiscale visualizations  “Design Patterns”

42. Future Work  Designing new patterns  Transitions between levels-of-detail Communicate parent-child relationships Non-uniform branching Animation/dissolve/fade?  Data management Prefetching and caching of large data sets