Efficient querying of large process model repositories

Tao Jin, Jianmin Wang, M. La Rosa, A.H.M. Hofstede, ter, Lijie Wen

Research output: Contribution to journalArticleAcademicpeer-review

41 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)41-49
Number of pages9
JournalComputers in Industry
Volume64
Issue number1
DOIs
Publication statusPublished - 2013

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