PLMflow—Dynamic Business Process Composition and Execution by Rule Inference

  • Liangzhao Zeng
  • David Flaxer
  • Henry Chang
  • Jeng Jun-Jang 
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2444)


With the proliferation of the Internet and the wide acceptance of e-commerce, increasing numbers of business processes and services are offered by distributed and heterogeneous service providers. This has created the need to explicitly employ workflow management systems (WFMS) to coordinate and control the flows of services. One of the fundamental assumptions of existing WFMS is that workflow schemas are predefined. Such an assumption becomes impractical for dynamic business processes that must be altered and composed on the fly to meet changing business conditions. PLM flow proposes a dynamic workflow system that is capable of supporting non-deterministic processes such as those found in collaborative product design scenarios, where decisions made by collaborative partners necessitate the dynamic composition and modification of running workflows. Instead of building complex static workflows to accommodate an explosive number of possibilities, we advocate a business rule inference based system to dynamically generate and execute workflows. As a result, end users can focus on the business goals to be achieved, instead of having to create detailed control and data flows for the work at hand.


Business Process Rule Inference Execution Path Business Logic Business Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Liangzhao Zeng
    • 1
  • David Flaxer
    • 2
  • Henry Chang
    • 2
  • Jeng Jun-Jang 
    • 2
  1. 1.School of Computer Science and EngineeringUniversity of New South WalesUK
  2. 2.IBM T.J. Watson Research CenterUSA

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