1. The Prajna Project Utilities for Understanding Edward Swing

2. Why Another Toolkit? Most toolkits focus on Information Control Information retrieval (databases) Information presentation (displays) Linking to other information (cross-referencing) Less emphasis on information understanding Prajna: Sanskrit for “Wisdom” Provide tools and capabilities to enhance the user’s understanding

3. Knowledge Engineering Involves Developer and User Steps for understanding and applying the knowledge Explicit and inherent structures of data Identify secondary sources of information Add reasoning components

4. Prajna Design Java Utility Library Extensible Classes Allow developers to integrate specific features Integration Points for other Software Libraries, Products, and Utilities Still Evolving

5. Prajna Conceptual Design Data Source Data Accessor Semantic Reasoning Data Fusion Data Accessor Future Capabilities Databases, Data Files, Other Content Retrieves Data, Assembles Structures (Trees, Graphs, Grids, Datasets) Reasoning Layer: Augments Data Visualization DisplayData Export Representation Layer

6. Core Components Data Structures Trees, Node-link Graphs, Grids (Volumes) Geographic, Temporal Data Semantic Content: Data Accessors Common representation of data Access data from a variety of sources Semantic Reasoning Ontologies Intelligent Fusion Visualization Displays

7. Data Accessors Data Source Data Accessor TreeGraphGrid Data Set Data Elements StringsEnums IntegersMeasures LocationsDateTime Data Configuration Structures Fields Reasoners

8. Representation Components Visualization Display Data Export Prefuse Visualization Prajna Visualizations KMLGraphML JFreeChart Charts Serialized Objects (Servlet) Data Accessor Update Geographic Display

9. Advantages Easy Extensibility Configurable Data Accessors Regardless of underlying data Use data where it exists Uses other toolkits where appropriate New toolkit interfaces No reinventing the wheel Integrates easily into larger projects

10. Integration Points Data Accessors Endeca, XML formats, JDBC, SOLR, … Streaming Data (in development) Data Generation Common data formats: SVG, GraphML, … Visualization Displays Prefuse toolkit for visualizations JFreeChart for charting Geographic Data ESRI shape files Google Earth KML

11. Demo: Fusion with Reasoning Core data: CSV file of wine data Fields include cost, winery, region, flavors, vintage year, review score and date, etc. 57000+ records Not clean data Some fields with multiple values (e.g., flavors) Additional information: Dbpedia access to determine grape species GeoNames access to determine region location Wine Ontology for additional information Display data in List, Map or Chart

12. Demo: Wine Data WebApp Core Data: Wine data, accessed from Endeca Information Access Platform Faceted Navigation Web Interface Allows easy search, filtering Standard Endeca capability Augmented Data Wine Ontology, GeoNames Food Ontology for matching Food to Wine Displays Google Maps display Simile Timeline

13. Demo: VAST Cell Phone Challenge Scenario: Phone network analysis of Paraiso leadership over 10-day period Data Provided: cell phone call records (CSV) Fields included ID of caller, receiver, cell phone tower, time and duration of call Auxiliary data: Map with estimated locations of cell towers Goal: identify key personnel, changes to the social network over time period Displays: Network, Map, Statistical Charts, Prefuse Force-directed display

14. Further Information Other Applications Other VAST contest entries Data Fusion & Visualization applications Prajna released to SourceForge: http://sourceforge.net/projects/prajna Includes various demonstration projects Contact Information edswing@comcast.net or deswing@vsticorp.com edswing@comcast.netdeswing@vsticorp.com