1 / 14

Architectural Support for Database Visualization

Architectural Support for Database Visualization. Dennis Groth Indiana University Computer Science 2002. Talk Structure. Problem Motivation Overview of Visualization Process Mapping Summary and Future Research. Motivation. What is visualization?

Thomas
Download Presentation

Architectural Support for Database Visualization

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Architectural Support for Database Visualization Dennis Groth Indiana University Computer Science 2002

  2. Talk Structure • Problem Motivation • Overview of Visualization Process • Mapping • Summary and Future Research Dennis Groth

  3. Motivation • What is visualization? “the act or process of interpreting in visual terms or of putting into visible form.” [Webster’s] “Transforming the symbolic into the geometric.” [McCormick et al, 1987] “The binding (or mapping) of data to representations that can be perceived.” [Foley, 1994] Dennis Groth

  4. The Goal of Visualization • Gain insight into data • Understand the “whole” • Identify relationships • Dynamic interaction • Different than presentation graphics: • Used to communicate information to others Dennis Groth

  5. Scientific Visualization • Visual representation of scientific data. Rainfall in Peru over 3 day period [Goldberg et al, 1987] Dennis Groth

  6. Information Visualization • Visual representation of abstract data. [Document Spiral , Cugini et al] Dennis Groth

  7. What’s the Difference? • Scientific data: • Often, already numeric • Natural mapping to coordinates • Abstract data: • May not be numeric (No order, No scale) • Mappings must be defined or constructed Dennis Groth

  8. Visualization Process (KDD) Dennis Groth

  9. Architecture Dennis Groth

  10. Applying a Map Output Input Data Map t1 t2 t3 . . . tn m1 m2 m3 . . . mn Dennis Groth

  11. Database Activities SalaryRank Age Select SalaryRank,Age, Count(*) From Employee, SalaryMap Where Employee.Salary = SalaryMap.Salary Group By SalaryRank,Age Dennis Groth

  12. User Interaction • Rotation, Translation, Scaling • Drill-down queries • Select data-points for use in other contexts • Scatterplots, barcharts, parallel coordinates, surface plots, line, … • Combining plots • Scaled independently or dependently • Overlay, Offset, Tile Dennis Groth

  13. Summary and Future Work • Architecture supporting visualization • Mapping language (Declarative approach) • Implementation • Future Research • Visualization • Extensions driven by applications • Document search • Cluster visualization • Data Mining • Association rule discovery Dennis Groth

More Related