On Measuring Process Model Similarity Based on High-Level Change Operations

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

102 Citations (Scopus)


For various applications there is the need to compare the similarity between two process models. For example, given the as-is and to-be models of a particular business process, we would like to know how much they differ from each other and how we can efficiently transform the as-is to the to-be model; or given a running process instance and its original process schema, we might be interested in the deviations between them (e.g. due to ad-hoc changes at instance level). Respective considerations can be useful, for example, to minimize the efforts for propagating the schema changes to other process instances as well. All these scenarios require a method to measure the similarity or distance between two process models based on the efforts for transforming the one into the other. In this paper, we provide an approach using digital logic to evaluate the distance and similarity between two process models based on high-level change operations (e.g. to add, delete or move activities). In this way, we can not only guarantee that model transformation results in a sound process model, but also ensure that related efforts are minimized.
Original languageEnglish
Title of host publicationConceptual modeling - ER 2008
Subtitle of host publication27th International Conference on Conceptual Modeling, Barcelona, Spain, October 20-24, 2008
EditorsQing Li
Place of PublicationLondon
Number of pages17
ISBN (Print)978-3-540-87876-6
Publication statusPublished - 2008

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Verlag
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • EWI-13598
  • IR-62482
  • METIS-251220
  • SCS-Services

Fingerprint Dive into the research topics of 'On Measuring Process Model Similarity Based on High-Level Change Operations'. Together they form a unique fingerprint.

Cite this