Every apprentice needs a master: Feedback-based effectiveness improvements for process model matching

摘要

Process models are a central element of modern business process management technology. When adopting such technology, organizations inevitably establish process model collections which, depending on the degree of adoption, can reach sizes of thousands of models. Process model matching techniques are intended to assist experts in the management of such large collections, e.g., in querying the collections and in comparing process models. Yet, as demonstrated in comparative evaluations, existing techniques struggle to achieve a high effectiveness on real-world datasets, limiting their practical applicability. This is partly due to these techniques being fully automated and relying on universal knowledge bases that insufficiently represent the domain semantics of model collections.To increase effectiveness and to progress on the path to practical applicability, we pursue the idea of integrating expert feedback into the matching process, so as to continuously update the knowledge base and achieve a better domain adaptation. In particular, we present ADBOT, a matching technique that relies on expert feedback in terms of corrected matching results. Our contributions are twofold. First, we introduce different strategies to utilize expert feedback in the matching process and to improve its effectiveness. Second, we provide heuristics for guiding experts through a model collection intended to reduce the amount of collected feedback while still maximizing the gains of learning from it. Based on five separate real-world datasets we provide empirical evidence towards the feasibility of our matcher. In the experiments, ADBOT (i) achieves high f-measures of up to .90, (ii) improves the effectiveness of baseline matchers by up to 88%, (iii) yields high recall values due to the detection of correspondences that automated matchers fail to achieve, and (iv) still increases effectiveness when the feedback contains errors. We also discuss evidence that substantiates ADBOT's individual components, amongst others demonstrating that the guidance heuristics can maximize effectiveness, while minimizing human effort. (C) 2020 Elsevier Ltd. All rights reserved.