Multiscale Visualization Using Data Cubes

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

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

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

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

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

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 This is a multiscale visualization! Dataabstraction

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 Zooming Possible zoom

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 y-axis zoom x-axis zoom

25. Design Patterns

26. Design Patterns • 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

28. Thematic Maps

29. Thematic Maps

30. Thematic Maps

31. Chart Stacks

32. Chart Stacks

33. Chart Stacks

34. 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