Advanced Data Visualization for Database Clutter Reduction

1. Allison WoodruffUniversity of California, Berkeley Clutter Reduction in Database Visualization

2. Motivation • Clutter can have negative effects • Decreased user performance • Diminished visual appeal • ...

3. Related work • Semantic zoom • SDMS, Pad, Pad++ • Filters/Highlighting • IVEE, Magic Lenses, EDV • Focus + context • Fisheyes

4. Outline • Data lineage • Data Engineering ‘97 • Buffering of intermediate results • Semantic zoom in DataSplash • Density width bars • Non-uniform data • Semi-automated construction • Goal-directed zoom

5. Data lineageMotivation • Earth scientist • Processes input data • Views result • Detects anomaly • Queries system • System • Tracks at coarse level • Fails to track at fine-grained level ?

6. Data lineageInversion of a function • User wants to identify parts of the input that map onto a given subset of the output • Unfortunately, a function that inverts perfectly doesn’t always exist Input Output f Perfect inversion

7. Data lineageWeak inversion • Often a weak inversion function exists • Doesn’t find perfect inverse • Finds a weak inverse that has certain guaranteed properties with relationship to the perfect inverse • There are rules for combining weak inverses to preserve properties Input Output f Weakinversion

8. Data lineageSummary • Software system • Expert users register weak inversion functions • User specifies anomaly and desired properties • System designs an execution plan to generate and combine weak inverses • Method to support fine-grained data lineage • Relies on limited information about processing steps • Provides imperfect inversions along with qualitative guarantees about the accuracy of these inversions • Eliminates much of the irrelevant source data

9. Outline • Data lineage • Buffering of intermediate results • Visual Languages ‘95 • Semantic zoom in DataSplash • Density width bars • Non-uniform data • Semi-automated construction • Goal-directed zoom

10. BufferingTioga • Dataflow language for visualization • Nodes are database procedures • Inputs and final results are stored in database tables • Intermediate results not stored • All results may be viewed by users

11. BufferingMotivation • Users revisit intermediate results • Debugging • Tuning • Data lineage (manual) • System revisits intermediate results • Re-execute with new inputs • Animation • Data lineage (weak inversion)

12. BufferingApproach • Buffer intermediate results to minimize recomputation over a sequence of queries to intermediate nodes

13. computed query buffered from Step 1 guarded BufferingExample scenario Step 1 At each step, the buffering algorithm must choose what to keep Step 2 Buffered nodes eliminate the need to compute their ancestors

14. BufferingResults • New heuristics are the most effective • Traditional methods are the least effective Offline Graph structure and user movement Graph structure Relative execution time Random Traditional (FIFO) Typical Randommoves Bushygraphs Variablesizes Largegraphs Dataflow graph structure and user movement

15. Outline • Data lineage • Buffering of intermediate results • Semantic zoom in DataSplash • Visual Languages ‘94, Visual Databases ‘95,Data Engineering ‘96 • Density width bars • Non-uniform data • Semi-automated construction • Goal-directed zoom

16. Semantic zoom in DataSplashMotivation • Definition of semantic zoom • Generally, two-dimensional canvas • User can pan and zoom • Objects change as the user zooms • Behavior of objects during zooming usually programmed by experts • Goal: make semantic zoom end-user programmable

17. Semantic zoom in DataSplashLayer manager • DataSplash objects are organized into layers • Each layer appears as a vertical layer bar in the layer manager • Layer bar shows elevation range at which layer is visible • Users can directly manipulate the bars Highelevation Low elevation

18. Semantic zoom in DataSplashView from a high elevation At the user’s current elevation, only the state outline layer is visible

19. Semantic zoom in DataSplashView from an intermediate elevation The cities circles layer becomes visible when the user zooms

20. Semantic zoom in DataSplashView from a low elevation The graph layer replaces the circles layer

21. Outline • Data lineage • Buffering of intermediate results • Semantic zoom in DataSplash • Density width bars • Advanced Visual Interfaces ‘98 • Non-uniform data • Semi-automated construction • Goal-directed zoom

22. Density width barsMotivation This visualization seems to have appropriate detail...

23. Density width barsMotivation, cont. …but the same visualization is cluttered at a higher elevation.

24. Density width barsApproach • Cartographic Principle of Constant Information Density (derived from Töpfer ’66): the number of objects/area should be constant at any scale • More generally, the amount of information should remain constant as the user pans and zooms • Visual Information Density Adjuster (VIDA) gives users feedback about the density of their applications

25. Density width barsDensity functions • Density functions • Input: region in canvas • Output: measurement of density • System-provided functions • Number of objects, number of vertices • User-defined density functions • There are many complex density functions • The number of edges between nodes • The number of text objects that overlap • ...

26. Density width barsVisual feedback about density • Width bars • The width of each layer bar at a given elevation is proportional to the layer’s average density at that same elevation • Tick marks • Colored to show cumulative density (red is too dense) • User slides the bars around, trying to maximize the number of green tick marks

27. Density width barsWidth bars in our example

28. Density width barsAfter the user has made adjustments

29. Outline • Data lineage • Buffering of intermediate results • Semantic zoom in DataSplash • Density width bars • Non-uniform data • In preparation • Semi-automated construction • Goal-directed zoom

30. Non-uniform dataMotivation • Width bars ensure uniformity in the z dimension • Many data sets are non-uniform in the x and y dimensions

31. Non-uniform dataApproach • Extend the Principle of Constant Information Density to subdivisions of the canvas • Break the screen into a grid • For each subdivision of the screen, choose a valid combination of layers with appropriate density • Allow user to specify constraints on combinations of layers • MUTUALLY EXCLUSIVE (city can be a dot or a circle) • ADDITIVE (states outlines can appear alone or with city circles)

32. Population visualization Before After

33. Housing cost/Income visualization Before After

34. Outline • Data lineage • Buffering of intermediate results • Semantic zoom in DataSplash • Density width bars • Non-uniform data • Semi-automated construction • Advanced Visual Interfaces ‘98 • Goal-directed zoom

35. Semi-automated constructionMotivation • Previously, we considered using the Principle of Constant Information Density to decide when to display layers • We could also try to decide the contents of layers

36. Semi-automated construction Operations to decrease density Original Select Aggregate Reclassify Change shape Reduce size Remove attribute association Change color

37. Semi-automated constructionTransformation space • These operations can generate a huge number of options (“transformations”) • We would like to present transformations visually to the user

38. Semi-automated constructionPortals • Windows onto other canvases • Graphical links

39. Semi-automated construction The transformation canvas • Transformations presented to user in a “transformation” canvas • Each transformation appears as a portal • When the user zooms, the visualizations all change • How should user navigate the transformation space?

40. Outline • Data lineage • Buffering of intermediate results • Semantic zoom in DataSplash • Density width bars • Non-uniform data • Semi-automated construction • Goal-directed zoom • CHI ‘98

41. Goal-directed zoomMotivation • Naïve zoom • Independent control of zoom and representation • Semantic zoom • Elevation implies representation • Goal-directed zoom • Representation implies elevation

42. Goal-directed zoomFortune 500 visualization • Displayed using VIDA’s technique for non-uniform data • Outliers have more detailed representation # employees % profit

43. Goal-directed zoomExample When user selects an object, a menu of representations appears When user selects an item from the menu, VIDA pans and zooms until that representation appears at appropriate visual detail