中文摘要：

符合性检测方法作为比较和关联事件日志与流程模型的技术,是三大核心流程挖掘技术之一,可用于量化符合性和诊断偏差.BPMN 2.0模型具有丰富的表达能力,能够表达多实例、子流程、边界事件、OR网关等多种复杂模式,但是目前还没有针对这些复杂模式的BPMN 2.0模型符合性检测算法.针对该问题,提出了基于对齐的BPMN 2.0模型符合性检测算法Acorn,该算法支持上述多种复杂模式.在深入分析BPMN 2.0模型中多种复杂模式的具体语义并分析其具体使能情况的基础上,Acorn算法引入对齐操作,利用A＊搜索算法寻找到代价最小的匹配轨迹,同时引入虚拟代价和预估代价来对A＊算法进行搜索空间的优化,最后根据最佳匹配轨迹来计算模型与日志的契合度.实验表明,Acorn算法能够正确有效地计算带有复杂模式的BPMN 2.0模型与日志之间的契合度,且虚拟代价和预估代价的引入,大大减少了搜索空间,有效提高了算法的运行速度.

英文摘要：

Process mining is an emerging discipline providing comprehensive sets of tools to provide fact-based insights and to support process improvements. This new discipline builds on process modeldriven approaches and data-centric analysis techniques such as machine learning and data mining. Conformance checking approaches, i. e. , techniques to compare and relate event logs and process models, are one of the three core process mining techniques. It is shown that conformance can be quantified and that deviations can be diagnosed. BPMN 2. 0 model has so powerful expression ability that it can express complex patterns like multi-instance, sub-process, OR gateway and boundary event. However, there is no existing conformance checking algorithm supporting such complex patterns. To solve this problem, this paper proposes an algorithm （Acorn） for conformance checking for BPMN 2. 0 model, which supports aforesaid complex patterns. The algorithm uses A＊ algorithm to find the minimum cost alignment, which is used to calculate fitness between BPMN 2. 0 model and the log. In addition, virtual cost and expected cost are introduced for optimization. Experimental evaluations show that Acorn can find the best alignment by exploiting the meanings of BPMN 2. 0 elements correctly and efficiently, and the introduction of virtual cost and expectation cost indeed reduces the search space.