Overcoming individual process model matcher weaknesses using ensemble matching
| Autor: | Heiner Stuckenschmidt, Hajo A. Reijers, Christian Meilicke, Elena Kuss, Henrik Leopold |
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| Přispěvatelé: | Software and Sustainability (S2), Network Institute, Business Informatica |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Matching (statistics)
Information Systems and Management Process modeling Optimal matching SDG 16 - Peace Computer science 02 engineering and technology Machine learning computer.software_genre Markov Logic Schema matching Management Information Systems Domain (software engineering) Matching quality Arts and Humanities (miscellaneous) 020204 information systems 3-dimensional matching 0202 electrical engineering electronic engineering information engineering Developmental and Educational Psychology Ensemble matching Markov chain business.industry SDG 16 - Peace Justice and Strong Institutions Process (computing) Justice and Strong Institutions Process model matching 020201 artificial intelligence & image processing Artificial intelligence Data mining business computer Information Systems |
| Zdroj: | Meilicke, C, Leopold, H, Kuss, E, Stuckenschmidt, H & Reijers, H A 2017, ' Overcoming individual process model matcher weaknesses using ensemble matching ', Decision Support Systems, vol. 100, no.-, pp. 15-26 . https://doi.org/10.1016/j.dss.2017.02.013 Decision Support Systems, 100(-), 15-26. North Holland |
| ISSN: | 0167-9236 |
| DOI: | 10.1016/j.dss.2017.02.013 |
| Popis: | In recent years, a considerable number of process model matching techniques have been proposed. The goal of these techniques is to identify correspondences between the activities of two process models. However, the results from the Process Model Matching Contest 2015 reveal that there is still no universally applicable matching technique and that each technique has particular strengths and weaknesses. It is hard or even impossible to choose the best technique for a given matching problem. We propose to cope with this problem by running an ensemble of matching techniques and automatically selecting a subset of the generated correspondences. To this end, we propose a Markov Logic based optimization approach that automatically selects the best correspondences. The approach builds on an adaption of a voting technique from the domain of schema matching and combines it with process model specific constraints. Our experiments show that our approach is capable of generating results that are significantly better than alternative approaches. |
| Databáze: | OpenAIRE |
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