Efficient querying of large process model repositories

  • Authors:

  • Tao Jin;Jianmin Wang;Marcello La Rosa;Arthur Ter Hofstede;Lijie Wen

  • Affiliations:

  • Department of Computer Science and Technology, Tsinghua University, Beijing, China and School of Software, Tsinghua University, Beijing 100084, China;School of Software, Tsinghua University, Beijing 100084, China and Key Laboratory for Information System Security, Ministry of Education, China and Tsinghua National Laboratory for Information Sci ...;Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia and NICTA Queensland Research Lab, Australia;Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia and Eindhoven University of Technology, Eindhoven, The Netherlands and NICTA Queensland Research Lab, Aust ...;School of Software, Tsinghua University, Beijing 100084, China and Key Laboratory for Information System Security, Ministry of Education, China and Tsinghua National Laboratory for Information Sci ...

  • Venue:
  • Computers in Industry
  • Year:
  • 2013

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Abstract

Recent years have seen an increased uptake of business process management technology in industries. This has resulted in organizations trying to manage large collections of business process models. One of the challenges facing these organizations concerns the retrieval of models from large business process model repositories. For example, in some cases new process models may be derived from existing models, thus finding these models and adapting them may be more effective and less error-prone than developing them from scratch. Since process model repositories may be large, query evaluation may be time consuming. Hence, we investigate the use of indexes to speed up this evaluation process. To make our approach more applicable, we consider the semantic similarity between labels. Experiments are conducted to demonstrate that our approach is efficient.