1. VizDB A tool to support Exploration of large databases By using Human Visual System To analyze mid-size to large data

2. Data Mining Techniques Implements several data mining techniques Pixel-oriented Techniques (Spiral, Axes, and Grouping Techniques) Parallel Coordinates Stick Figures Exploration of unto a million data values

3. Concept The basic idea for visualizing the data is to map the distances to colors and represent each data value by one or multiple colored pixels. Interactivity is the key !

4. Requirement Feedback required when query returns unexpected results Interactivity allows immediate feedback from a modified query Configurable tool, that allows various forms of data visualization techniques Using the human vision system for pattern recognition

5. Basic Technique Sort query data w.r.t. the relevance and map relevance factors to colors Highest relevance factor in the center Yellow-Green-Blue-Red-Black in decreasing order of relevance. Separate window for each selection predicate in the query Multiple windows make multi-dimensional visualization

6. Mapping 2-D To The Axes Visualization of inherently 2D or 3D data is not dealt with in VizDB Where no inherent 2D semantics of data exist, VizDB is a valuable tool. Use of two axes for two dimensions. Positive as well as negative values displayed. Some space may be wasted.. (Why?)

7. Grouping Each area is arranged in a rectangular spiral shape according to relevance factors Coloring is similar to the previous method Grouping allows data similar in one dimension to be grouped together. Data in multiple dimensions are represented as clusters of pixels Good for larger dimensionality

8. Interactive Data Exploration Dynamic Query Modification Techniques Feedback on the results – Change in color means change in values that are “relevant” – Change in structure means overall distribution of data has changed Sliders for discrete as well as continuous values Initial Query is SQL or “Gradi”

9. Calibrations Calculation of “relevance” factor can be calibrated by the user Starting and ending values for various numeric data – Eg: Blood samples count

10. What about complex queries? Multiple layers of windows for complex queries using nested AND and OR operators Data that satisfies ALL joins is yellow. The rest is colored according to number of criteria met Works well with the relational databases

11. Implementations C++ with Motif using X Windows on HP 7xx Currently being ported to Linux (I couldn’t get this working! )

12. Adding new techniques More Info Viz. Techniques can be integrated with the system. Latest version supports Parallel co- ordinates, Stick Figures, Pan and zoom techniques New Stuff !!

13. Applications Molecular Biology - to find possible docking regions by identifying sets surface points with distinct characteristics. Database of geographical data Environmental Data NASA Earth observation data

14. Future Work Automatic generation of queries that correspond to data in specific regions (Select some data, and the SQL query that matches that data will get generated.. Time series visualization Cool !!

15. Thank You The presentation slides are available at http://filebox.vt.edu/users/adatey/research/Viz DB.ppt A small color picture that shows different techniques http://filebox.vt.edu/users/adatey/research/Vis DBHandout.eps