Petri Nets Based Recognition of Model Deviation Domains and Model Repair
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摘要: 过程挖掘技术能够通过事件日志建立过程模型，一致性检测技术能够发现过程模型和观察行为间的偏差.然而，现有的过程挖掘技术着重于发现偏差，不易于修正偏差.因此，利用一致性检测技术和工作流网模型的动态特性，提出一种基于Petri网的模型偏差域识别方法和模型修正技术(静态模型修正和动态模型修正).通过跟踪token流向，有效地识别模型偏差域，并对其进行修正，特别是能够正确修正具有循环结构、选择结构的复杂实际流程.最后，通过与其他方法的对比实验和分析，验证了本文方法的有效性和正确性.Abstract: Process mining techniques can be used to discover process models from event logs. Event logs and process model can be contrasted by conformance checking techniques. And conformance checking techniques can be used to detect the deviations between observed behaviors and process model. However, existing techniques of process mining concern with discovering these deviations, but not support to repair the process model easily and make the process model more related to the real process. So in this paper we propose a token-based identification method of model deviation domains and a token-based technique of model repair (static model repair and dynamic model repair) through techniques of conformance checking and dynamic behaviors of workflow net. Model deviation domains can be identified effectively though the flow direction of token. We can repair process model according to model deviation domains. And we also can repair the real complex process accurately which has the structures of complicated circulation and choice. In this paper, the effectiveness and the correctness of techniques are illustrated through contrast experiment and analysis with other techniques.
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Keywords:
- conformance checking /
- model repair /
- deviation domains /
- workflow net /
- token
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