摘要:现有的过程挖掘算法依赖于"aba"模式来挖掘2度循环，而满足局部完备性的日志文件中不一定出现该模式.为此，扩展了经典Alpha算法，提出了α^L+算法，用于从没有"aba"模式的日志文件中挖掘出2度循环.首先建立任务间的次序向量矩阵，用于抽象2度循环结构的变体结构；然后从全局视角，根据事件的出现次数及位置来区分2度循环和并发关系；最后提出紧邻度和回路抽象，以排除并发分支上同类型循环带来的干扰.实验结果表明，与现有的挖掘算法相比，α^L+算法能够从具有"aba"模式或不具有"aba"模式的日志文件中挖掘出2度循环.此外，该算法实现且集成在开源框架ProM中.



Abstract:The current research in mining length-two loops depends on "aba" pattern. However, the pattern does not necessarily appear in the logs that satisfies local completeness. This research aims at finding ways to mine length-two loops without the pattern. It results in a new algorithm (α^L+-algorithm) that is based on the α-algorithm. First, an order vector matrix is established by tasks in logs to abstract variant structures of length-two loops. Then, distinction between loops and concurrency structure is obtained by event's frequency and location in traces. Finally, proximity and circuit abstraction are used to eliminate the interference caused by the concurrent branches. The experimental results show that the α^L+-algorithm can handle length-two loops with or without "aba" pattern. In addition, the α^L+-algorithm is implemented in the ProM tool.



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