1. International Semantic Web Doctoral Symposium Research Topic: Representing Discrete-Event Simulation Process-Interaction Models using the Web Ontology Language - OWL November 7, 2005 Lee W. Lacy PhD Candidate University of Central Florida

2. 2 PIMODES Research Overview Discrete-event simulation models support operations research and other applications These models have historically been represented in vendor-specific file formats that have made sharing/interchange difficult Research is being performed to develop an OWL ontology that will provide a neutral interchange description of these types of models. The ontology is being scoped to a particular type of discrete event simulation model descriptions – those that adhere to the process- interaction world view The interchange of simulation models using the ontology will be demonstrated using web-based software

3. 3 Presentation Outline Purpose Description Goal Statement Methodology Evaluation Summary

4. 4 Purpose Description Simulation Model Interchange Challenges Scoping the Problem Subject Domain Benefits of Interchange

5. 5 Why Interchange Models? Leverage investment in model development through reuse –Higher quality through reuse of validated models –Speed development lifecycle –Reduce development costs Enable competition of model development environments and compliant execution engines –Potential software manufacturer push-back if not presented correctly –Need to sell the HTML and XML business models Shift model development emphasis from programming to model quality

6. 6 Scoping the Proposed Research Simulation data interchange topic broad Various types of simulation data Emphasis shifting from code to data Simulation models represent one type of data in newer “data-driven” systems Model is problem-specific while execution engine is problem-independent Discrete-event simulations represent one type of simulation Further scoped by “world view”

7. 7 Subject Domain

8. 8 Benefits of Model Interchange Better –Reuse of validated models Faster –Quicker to create new models by leveraging existing models Cheaper –Lower cost due to reuse instead of creation

9. 9 Goal Statement Develop an ontology to support Discrete Event simulation model interchange Ontology becomes a “de facto” language New ontology/language harmonizes the most important aspects of legacy languages Legacy models can then be converted to/from the new “lingua franca” – enabling interchange

10. 10 Methodology Use of OWL Ontologies Research Activities Research Plan Feedback Opportunities Anticipated Results

11. 11 Simulation Ontology Representations Proposed by Lacy (2004) and Miller & Fishwick (2004) Provides advantages over traditional (e.g., XML) approaches Requires the development of a meta-model Existing modeling languages have implicit ontologies New explicit ontologies in effect describes a new modeling language Mappings required from legacy languages to the new ontology

12. 12 Web Ontology Language - OWL OWL became a World Wide Web Consortium (W3C) Standard in February 2004 OWL will be used to define a Process-Interaction Modeling Ontology for Discrete-Event Simulations (PIMODES) Compliant instance files are represented using RDF/XML

13. 13 OWL’s Layered Architecture

14. 14 Research Plan

15. 15 A0 View

16. 16 A2 – Literature Search (¶2) Activities Formalize domain semantics (¶2.1) Survey existing discrete-event process-interaction (¶2.2) –Software packages (¶2.2.2) –Modeling languages (¶2.2.3) –Formalisms/Representation methods (¶2.2.4) Review related simulation information interchange research (¶2.3) Describe Semantic Web technology (¶2.4)

17. 17 Evaluation Develop sample model in ProcessModel and Arena Convert legacy model representations to DEPIM Convert DEPIM representation to legacy formats Compare converted models to original models

18. 18 Demonstration Data Flow

19. 19 Summary Simulation data is interchanged in a variety of ways Interchange is best performed with open standards OWL can be used to define an ontology for Discrete-Event Process-Interaction models Use of such an ontology can be demonstrated by converting legacy simulation model formats