Effective Utilization of Supervised Learning Techniques for Process Model Matching

The recent attempts to use supervised learning techniques for process model matching have yielded below par performance. To address this issue, we have transformed the well-known benchmark correspondences to a readily usable format for supervised learning. Furthermore, we have performed several experiments using eight supervised learning techniques to establish that imbalance in the datasets is the key reason for the abysmal performance. Finally, we have used four data balancing techniques to generate balanced training dataset and verify our solution by repeating the experiments for the four datasets, including the three benchmark datasets. The results show that the proposed approach increases the matching performance significantly

Overcoming individual process model matcher weaknesses using ensemble matching

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

Ähnlichkeitsbasierte Suche in Geschäftsprozessmodelldatenbanken

Die Wiederverwendung von Prozessmodellen bietet sich zur Reduzierung des hohen Modellierungsaufwands an. Allerdings ist das Auffinden von ähnlichen Modellen in großen Modellsammlungen manuell nicht effizient möglich. Hilfreich sind daher Suchmöglichkeiten nach relevanten Modellen, die als Vorlage zur Modellierung genutzt werden können. In dieser Arbeit werden Ansätze beschrieben, um innerhalb von Prozessmodellbibliotheken nach ähnlichen Modellen und Aktivitäten zu suchen