A Heuristic Approach for Discovering Reference Models by Mining Process Model Variants

C. Li, M.U. Reichert, Andreas Wombacher

Research output: Book/ReportReportProfessional

29 Downloads (Pure)

Abstract

Recently, a new generation of adaptive Process-Aware Information Systems (PAISs) has emerged, which enables structural process changes during runtime while preserving PAIS robustness and consistency. 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 can not 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.
Original languageUndefined
Place of PublicationEnschede
PublisherDatabases (DB)
Number of pages51
Publication statusPublished - Mar 2009

Publication series

NameCTIT Technical Report Series
PublisherCentre for Telematics and Information Technology, University of Twente
No.TR-CTIT-09-08
ISSN (Print)1381-3625

Keywords

  • METIS-263756
  • SCS-Services
  • EWI-15153
  • IR-65408

Cite this

Li, C., Reichert, M. U., & Wombacher, A. (2009). A Heuristic Approach for Discovering Reference Models by Mining Process Model Variants. (CTIT Technical Report Series; No. TR-CTIT-09-08). Enschede: Databases (DB).
Li, C. ; Reichert, M.U. ; Wombacher, Andreas. / A Heuristic Approach for Discovering Reference Models by Mining Process Model Variants. Enschede : Databases (DB), 2009. 51 p. (CTIT Technical Report Series; TR-CTIT-09-08).
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author = "C. Li and M.U. Reichert and Andreas Wombacher",
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Li, C, Reichert, MU & Wombacher, A 2009, A Heuristic Approach for Discovering Reference Models by Mining Process Model Variants. CTIT Technical Report Series, no. TR-CTIT-09-08, Databases (DB), Enschede.

A Heuristic Approach for Discovering Reference Models by Mining Process Model Variants. / Li, C.; Reichert, M.U.; Wombacher, Andreas.

Enschede : Databases (DB), 2009. 51 p. (CTIT Technical Report Series; No. TR-CTIT-09-08).

Research output: Book/ReportReportProfessional

TY - BOOK

T1 - A Heuristic Approach for Discovering Reference Models by Mining Process Model Variants

AU - Li, C.

AU - Reichert, M.U.

AU - Wombacher, Andreas

PY - 2009/3

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N2 - Recently, a new generation of adaptive Process-Aware Information Systems (PAISs) has emerged, which enables structural process changes during runtime while preserving PAIS robustness and consistency. 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 can not 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.

AB - Recently, a new generation of adaptive Process-Aware Information Systems (PAISs) has emerged, which enables structural process changes during runtime while preserving PAIS robustness and consistency. 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 can not 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.

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Li C, Reichert MU, Wombacher A. A Heuristic Approach for Discovering Reference Models by Mining Process Model Variants. Enschede: Databases (DB), 2009. 51 p. (CTIT Technical Report Series; TR-CTIT-09-08).