Overcoming individual process model matcher weaknesses using ensemble matching
Overcoming individual process model matcher weaknesses using ensemble matching
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DOI：
10.1016/j.dss.2017.02.013
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发表时间：
2017-08
期刊：
影响因子：
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通讯作者：
Christian Meilicke;H. Leopold;Elena Kuss;H. Stuckenschmidt;H. Reijers
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文献类型：
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作者：
Christian Meilicke;H. Leopold;Elena Kuss;H. Stuckenschmidt;H. Reijers
近年来，已经提出了相当多的过程模型匹配技术。这些技术的目标是识别两个过程模型的活动之间的对应关系。然而，2015年流程模型匹配竞赛的结果表明，仍然没有普遍适用的匹配技术，每种技术都有其优缺点。对于给定的匹配问题，很难甚至不可能选择最佳技术。我们建议通过运行匹配技术的集合并自动选择所生成的对应关系的子集来科普这个问题。为此，我们提出了一种基于马尔可夫逻辑的优化方法，自动选择最佳的对应关系。该方法建立在一个自适应的投票技术从域的模式匹配，并结合它与过程模型的特定约束。我们的实验表明，我们的方法是能够产生的结果是显着优于替代方法。
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.