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A framework for querying graph-based business process models

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Published:26 April 2010Publication History

ABSTRACT

We present a framework for querying and reusing graph-based business process models. The framework is based on a new visual query language for business processes called BPMN-Q. The language addresses processes definitions and extends the standard BPMN visual notations for modeling business processes for its concrete syntax. BPMN-Q is used to query process models by matching a process model graph to a query graph. Moreover, the reusing framework is enhanced with a semantic query expander component. This component provides the users with the flexibility to get not only the perfectly matched process models to their queries but also the models with high similarity. The query engine of the framework is built on top of traditional RDBMS. A novel decomposition based and selectivity-aware relational processing mechanism is employed to achieve an efficient and scalable performance for graph-based BPMN-Q queries.

References

  1. A. Awad. BPMN--Q: A language to query business processes. In EMISA, 2007.Google ScholarGoogle Scholar
  2. A. Awad, G. Decker, and M. Weske. Efficient Compliance Checking Using BPMN-Q and Temporal Logic. In BPM, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. A. Awad, A. Polyvyanyy, and M. Weske. Semantic querying of business process models. In EDOC, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. A. Awad and F. Puhlmann. Structural Detection of Deadlocks in Business Process Models. In BIS, 2008.Google ScholarGoogle Scholar
  5. G. Graefe. Sorting And Indexing With Partitioned B--Trees. In CIDR, 2003.Google ScholarGoogle Scholar
  6. T. Grust, M. Mayr, J. Rittinger, S. Sakr, and J. Teubner. A SQL:1999 Code Generator for the Pathfinder XQuery Compiler. In SIGMOD, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. T. Grust, S. Sakr, and J. Teubner. XQuery on SQL Hosts. In VLDB, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. R. Laue and A. Awad. Visualization of business process modeling anti patterns. In VFfP, 2009.Google ScholarGoogle Scholar
  9. S. Sakr. GraphREL: A decomposition-based and selectivity-aware relational framework for processing sub-graph queries. In DASFAA, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. G. Salton, A. Wong, and C. S. Yang. A Vector Space Model for Automatic Indexing. Commun. ACM, 18(11), 1975. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. J. Teubner, T. Grust, S. Maneth, and S. Sakr. Dependable Cardinality Forecats for XQuery. In VLDB, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Matthias Weidlich, Gero Decker, Alexander Großkopf, and Mathias Weske. BPEL to BPMN: The myth of a straight-forward mapping. In OTM, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. A framework for querying graph-based business process models

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    • Published in

      cover image ACM Other conferences
      WWW '10: Proceedings of the 19th international conference on World wide web
      April 2010
      1407 pages
      ISBN:9781605587998
      DOI:10.1145/1772690

      Copyright © 2010 International World Wide Web Conference Committee (IW3C2)

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 26 April 2010

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      Overall Acceptance Rate1,899of8,196submissions,23%

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