基于统计α算法的过程挖掘

Process mining based on statistical α-algorithm

过程挖掘算法是从管理信息系统产生的事件日志中提取信息、发现知识并实现工作流建模的工具,也是目前工作流最主要的建模工具。然而现有的过程挖掘算法存在准确度较低、运行时间长和拟合度过高等问题,影响最终工作流模型的准确率。提出了一种基于统计α算法的过程挖掘算法,在保证算法较高的准确率和合适的拟合度的同时,降低算法运行时间,保证了算法的效率。首先,提出了重名活动识别算法,作为过程挖掘的预处理活动,提高了算法的准确性;其次,提出了统计α算法作为过程挖掘核心算法,有效消除了事件日志中噪声的影响;最后,提出了新的非自由选择结构识别算法,进一步提高了算法的鲁棒性和准确率。通过仿真实验和真实案例验证了该算法在准确率和运行时间上的优越性。

Workflow technology is widely used in business process management. However,there are still many problems during the execution of business process because of the imperfect workflow model. Process mining is the most useful tool of workflow modeling,which can obtain objective and valuable information from event logs and build process model. Nevertheless,the existing process mining algorithms still have some problems,such as low accuracy,long operation time and overfitting,which will decreace the accuracy of the workflow model. This paper proposed a new process mining algorithm based on statistical α-algorithm,which can not only ensure the accuracy and suitable fitness,but also decrease the operation time. First,cognominal activity identification rules were proposed to be the pre-treated process of process mining,which could improve the accuracy of algorithm. Second,statistical α-algorithm was proposed as the core algorithm of process mining to eliminate the influence of noise in event logs. Moreover,a new algorithm was proposed to identify non-freechoice constructs,which improved the robustness and accuracy of the algorithm. The accuracy and efficiency of the algorithm are verified by simulation and real case.