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Discovering Reference Models by Mining Process Variants Using a Heuristic Approach

  • Chen Li
  • Manfred Reichert
  • Andreas Wombacher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5701)

Abstract

Recently, a new generation of adaptive Process-Aware Information Systems (PAISs) has emerged, which enables structural process changes during runtime. Such flexibility, in turn, leads to a large number of process variants derived from the same model, but differing in structure. Generally, such variants are expensive to configure and maintain. This paper provides a heuristic search algorithm which fosters learning from past process changes by mining process variants. The algorithm discovers a reference model based on which the need for future process configuration and adaptation can be reduced. It additionally provides the flexibility to control the process evolution procedure, i.e., we can control to what degree the discovered reference model differs from the original one. As benefit, we cannot only control the effort for updating the reference model, but also gain the flexibility to perform only the most important adaptations of the current reference model. Our mining algorithm is implemented and evaluated by a simulation using more than 7000 process models. Simulation results indicate strong performance and scalability of our algorithm even when facing large-sized process models.

Keywords

Process Variant Reference Model Candidate Model Change Operation Order Matrix 
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 2009

Authors and Affiliations

  • Chen Li
    • 1
  • Manfred Reichert
    • 2
  • Andreas Wombacher
    • 1
  1. 1.Computer Science DepartmentUniversity of TwenteThe Netherlands
  2. 2.Institute of Databases and Information SystemsUlm UniversityGermany

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