IT Revolutions. First International ICST Conference, IT Revolutions 2008, Venice, Italy, December 17-19, 2008, Revised Selected Papers

Research Article

Applying Business Process Re-engineering Patterns to optimize WS-BPEL Workflows

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  • @INPROCEEDINGS{10.1007/978-3-642-03978-2_14,
        author={Jonas Buys and Vincenzo Florio and Chris Blondia},
        title={Applying Business Process Re-engineering Patterns to optimize WS-BPEL Workflows},
        proceedings={IT Revolutions. First International ICST Conference, IT Revolutions 2008, Venice, Italy, December 17-19, 2008, Revised Selected Papers},
        proceedings_a={IT REVOLUTIONS},
        year={2012},
        month={5},
        keywords={business process re-engineering (BPR) service-oriented architectures (SOA) WS-BPEL business processes workflows},
        doi={10.1007/978-3-642-03978-2_14}
    }
    
  • Jonas Buys
    Vincenzo Florio
    Chris Blondia
    Year: 2012
    Applying Business Process Re-engineering Patterns to optimize WS-BPEL Workflows
    IT REVOLUTIONS
    Springer
    DOI: 10.1007/978-3-642-03978-2_14
Jonas Buys1,*, Vincenzo Florio1,*, Chris Blondia1,*
  • 1: University of Antwerp
*Contact email: jonas.buys@ua.ac.be, vincenzo.deflorio@ua.ac.be, chris.blondia@ua.ac.be

Abstract

With the advent of XML-based SOA, WS-BPEL shortly turned out to become a widely accepted standard for modeling business processes. Though SOA is said to embrace the principle of business agility, BPEL process definitions are still manually crafted into their final executable version. While SOA has proven to be a giant leap forward in building flexible IT systems, this static BPEL workflow model is somewhat paradoxical to the need for real business agility and should be enhanced to better sustain continual process evolution. In this paper, we point out the potential of adding business intelligence with respect to business process re-engineering patterns to the system to allow for automatic business process optimization. Furthermore, we point out that BPR macro-rules could be implemented leveraging micro-techniques from computer science. We present some practical examples that illustrate the benefit of such adaptive process models and our preliminary findings.