Open AccessJournal Article
The Process Model Matching Contest 2015
Gonçalo Antunes,Marzieh Bakhshandeh,José Borbinha,João Luís Cardoso,Sharam Dadashnia,Chiara Di Francescomarino,Mauro Dragoni,Peter Fettke,Avigdor Gal,Chiara Ghidini,Philip Hake,Abderrahmane Khiat,Christopher Klinkmüller,Elena Kuss,Henrik Leopold,Peter Loos,Christian Meilicke,Tim Niesen,Catia Pesquita,Timo Péus,Andreas Schoknecht,Eitam Sheetrit,Andreas Sonntag,Heiner Stuckenschmidt,Tom Thaler,Ingo Weber,Matthias Weidlich +26 more
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TLDR
This paper summarizes the setup and the results of the second edition of the Process Model Matching Contest in 2015, and provides short descriptions of all matching techniques that have been submitted for participation.Abstract:
Process model matching refers to the automatic identification of correspondences between the activities of process models. Application scenarios of process model matching reach from model validation over harmonization of process variants to effective management of process model collections. Recognizing this, several process model matching techniques have been developed in recent years. However, to learn about specific strengths and weaknesses of these techniques, a common evaluation basis is indispensable. The second edition of the Process Model Matching Contest in 2015 hence addresses the need for effective evaluation by defining process model matching problems over published data sets. This paper summarizes the setup and the results of the contest. Next to a description of the contest matching problems, the paper provides short descriptions of all matching techniques that have been submitted for participation. In addition, we present and discuss the evaluation results and outline directions for future work in the field of process model matching.read more
Citations
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Detecting Inconsistencies Between Process Models and Textual Descriptions
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Similarity of Business Process Models—A State-of-the-Art Analysis
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TL;DR: A semi-automatic approach that maps events to activities using insights from behavioral analysis and label analysis and techniques from natural language processing allow for a matching based on labels and external knowledge sources is developed.
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Listen to me: Improving Process Model Matching through User Feedback
TL;DR: An approach that performs matching in an iterative, mixed-initiative approach that determines correspondences between two models automatically, let the user correct them, and analyze this input to adapt the matching algorithm improves the matching quality.
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Towards the Automated Annotation of Process Models
Henrik Leopold,Christian Meilicke,Michael Fellmann,Fabian Pittke,Heiner Stuckenschmidt,Jan Mendling +5 more
TL;DR: This paper presents the first approach for automatically annotating process models with the concepts of a taxonomy, and builds on the corpus-based method of second-order similarity, different similarity functions, and a Markov Logic formalization.
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