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Table Lens

This paper discusses the Table Lens technique, which uses a focus+context approach to visually represent data tables, increasing viewable portion and ease of navigation. It also explores the manipulation of focus operations and the distortion function framework.

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Table Lens

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  1. Table Lens From papers 1 and 2 By Tichomir Tenev, Ramana Rao, and Stuart K. Card

  2. Overview • Uses focus+Context apporach • Context elements are represented graphically • Focus elements have text and graphic display

  3. Advantages • Increases viewable portion of table by 100 times • Ease of Navigation • Ease of Exploration

  4. Table Lens Focal Technique • Mutates layout of table • Does not bend any rows or columns

  5. Distortion Function Framework • DOI function: item -> value. Value indicates level of interest • DOI function controls how available space is allocated among items

  6. DOI in Table lens • DOI maps cell address to interest level • 2 of them, one for each dimension

  7. Manipulation of Focus Operations • Zoom- changes amount of space to focal area • Adjust- changes amount of contents viewed within focus area • Slide- changes location of focus area within the context

  8. User manipulation • Clicking at upper left corner- zooms all cells • Touching any region in context will slide current focus to that location • Grasping focus slides focus to that location

  9. Results • Apply data to baseball stats of 323 rows by 23 columns (7429 cells) • Display whole table on screen at one time

  10. Paper #2 Design 1 Nesting Focal Levels • Space allocated to each element is dependent on the focal level of element • 2 foci, Primary focus always inside region of secondary focus • 2ndary focus used for coarse navigation • Primary used for finer navigation

  11. Design II Controlling focal spans • Space allocated per data element dependent on focus level and parameter specified by user • Primary focus elements may vary in size • Spatial map at any time depends on History of user interaction

  12. Conclusion • Felt design 2 was the better design.

  13. Disadvantage • Works only for data tables which have have <= number of entries as pixel rows and each column has enough pixels wide to accommodate variables. • Paper #2 discusses how to improve it

  14. Polaris: A System for Query, Analysis and Visualization of Multi-dimensional Relational Databases Chris Stolte and Pat Hanrahan Standford University

  15. Polaris • Interactive exploration of large multi-dimensional databases • Expressive set of graphical displays • Uses tables to organize multiple graphs on a display

  16. Relational databases • Each row in table = basic entity (tuple) • Each column represents a field • Fields can be ordinal, or quantitative

  17. Visual Specification • Is the configuration of the fields of the tables on shelves • User does this by dragging and dropping fields onto shelves

  18. Visual Specification • Mapping of data sources to layers • # of rows, columns, and layers, and relative order • Selection of tuples from the database • Grouping of data within a pane • Type of graphic displayed in each pane • Mapping of data fields with retinal properties

  19. Table Algebra • Used to specify table configurations. Dragging and dropping implicitly does it • Operands are the names of the ordinal and quantitative fields of database • Operators (concatenation, cross, nest)

  20. Types of Graphics (Ordinal- Ordinal) • Axis variables are independent of each other R represents the fields encoded in the retinal properties of the marks Following slide shows sales and margin as a function of product type, month and state for items sold by coffee chain

  21. Ordinal-Quantitative Graphics • Bar charts, dot plots, Gantt chart • Quantitative variable is dependent of ordinal variable Figure 6c shows a case where a matrix of bar charts is used to study several functions of the independent variables product and month

  22. Quantitative-Quantitative Graphics • Discover causal relationships between the two quantitative variables. Figure 3e shows how flight scheduling varies with the region of the country the flight originated.

  23. Visual mappings • Encoding different fields of the data to retinal properties • Shape, Size, Orientation, Color • Used in the ordinal to ordinal example

  24. Generating Database Queries • 1. Selecting the Records

  25. Generating Database Queries • 2. Partitioning the records into pains • Putting retrieved records in their corresponding pane

  26. Generating Database Queries • 3. Transforming Records within the Panes • If aggregation, it is done here

  27. Results • Cut expenses for a national coffee store • Create table of scatterplots showing relationship between marketing costs and profit (Figure 6a) • Notice trend; certain products have high marketing costs with no or little profit

  28. Results • Used linked displays to determine that in New York several products are offering very little return despite high costs • Creates bar chart for products in New York

  29. Future Work • Exploring interaction techniques for navigating hierarchical structures of mulit-dim databases • Use selected mark in one display as the data input to another

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