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Reacting to HCI Devices: Initial Work Using Resource Ontologies with RAVE Dr. Ian Grimstead Richard Potter BSc(Hons). Presentation Structure. Aims and Objectives Background to Ontologies Background to RAVE Initial extension of RAVE Demonstration Questions and Answers. Aims.

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Presentation Structure

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  1. Reacting to HCI Devices:Initial Work Using Resource Ontologies with RAVEDr. Ian GrimsteadRichard Potter BSc(Hons)

  2. Presentation Structure • Aims and Objectives • Background to Ontologies • Background to RAVE • Initial extension of RAVE • Demonstration • Questions and Answers.

  3. Aims • To enhance RAVE • Extend resource-awareness • Include user input devices • To prototype ontological approach • In an existing system.

  4. Objectives • Implement “Widgets” • Enable Widgets to request interaction support • Enable RAVE to access Ontology • RAVE widgets announce interaction style required • Ontology finds best fit • Resource interfaced with RAVE for information exchange.

  5. Background to Ontologies • Ontology: Formal collection of inter-referenced taxonomies • Taxonomy – a hierarchical classification • Resources described using 6 taxonomies: • Hardware • User actions • Sensory experiences • Software interfaces • Software interface items • Variables.

  6. Resource description

  7. Categorisation • Each taxonomy categorises information, for example the user interaction taxonomy:

  8. Libraries and APIs • Sesame (RDF/RDFS repository) • OwlIm (Sesame ontology plugin) • Sesame-to-Jena • Jena (Ontology API and inference engine) • OWL/RDF/RDFS (Ontology implementation languages).

  9. Backgroundto RAVE • RAVE – Resource-Aware Visualization Environment • Supports collaborative visualization • Static datasets, or • Real-time feed from remote process • Remote process can be instrumented • RAVE can then steer a simulation.

  10. RAVE Client Internet or remote machine RAVE Client Visualization Data Raw Data RAVE Client Data Server Data Distribution • First component: Data Server • Acts as a distribution point & interpreter • Understands many types of data • Uses Java3D+Xj3D as importer

  11. Visual drawn on local machine Visualization Data Data Server Displaying Data • Second component: Active RAVE Client • “Active” – facilities to draw on its own • Accepts feed from Data Server • Presents images of data to user Active RAVE Client

  12. Computational Steering • Independent simulation can supply Data Server • Simulation code instrumented • Transmits scene creation and updates to Data Server • Data Server reflects updates • Multiple clients can view live simulation • Client interact with “widgets” • Steer simulation.

  13. Summary • Data Server reads data and distributes • Active Client renders locally • Data Server can link to live simulation • All resources shared where possible • Uses Java to support (most) platforms • Also: • Thin Client (PDA) renders via Render Server • But that’s another story.

  14. Resource support • Semantic reasoning over the ontology: • Appropriate resources can be chosen • Ontology also describes: • Each resource’s available software interfaces • Joins visualization to selected hardware • Supports intuitive interaction • Selects most appropriate resource.

  15. Resource support

  16. Initial Extensionto RAVE • Database to store ontology • RAVE extended with new Widget type • Issues: • Lack of driver support! • Only available: mouse / mouse with wheel • Shows proof of concept.

  17. Demonstration • We will show: • RAVE interacting with a molecular dynamics simulation • RAVE discovering available input devices • Ontology support automatically selecting available devices • RAVE being unaware of available input devices.

  18. Questions and Answers Questions… …and possibly answers?

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