A novel approach for process mining based on event types
A novel approach for process mining based on event types
复制标题
DOI：
10.1007/s10844-007-0052-1
复制
发表时间：
2007-07
影响因子：
3.4
通讯作者：
L. Wen;Jianmin Wang;Wil M.P. van der Aalst;Biqing Huang;Jiaguang Sun
中科院分区：
文献类型：
--
作者：
L. Wen;Jianmin Wang;Wil M.P. van der Aalst;Biqing Huang;Jiaguang Sun
尽管事务信息系统中的事件日志无处不在(参见WFM、ERP、CRM、SCM和B2B系统)，很少使用历史信息来分析底层流程。流程挖掘旨在通过提供从事件日志中发现流程、控制、数据、组织和社会结构的技术和工具来改进这一点，即流程挖掘的基本思想是通过挖掘事件日志中的知识来诊断业务流程。考虑到它的潜力和挑战，最近过程挖掘成为一个生动的研究领域也就不足为奇了。提出了一种新的基于开始和完成两种事件类型的流程挖掘方法。有关任务开始和完成的信息可用于显式检测并行性。该算法克服了现有α算法(如短环算法)的一些局限性，从而增强了流程挖掘的适用性。
Despite the omnipresence of event logs in transactional information systems (cf. WFM, ERP, CRM, SCM, and B2B systems), historic information is rarely used to analyze the underlying processes. Process mining aims at improving this by providing techniques and tools for discovering process, control, data, organizational, and social structures from event logs, i.e., the basic idea of process mining is to diagnose business processes by mining event logs for knowledge. Given its potential and challenges it is no surprise that recently process mining has become a vivid research area. In this paper, a novel approach for process mining based on two event types, i.e., START and COMPLETE, is proposed. Information about the start and completion of tasks can be used to explicitly detect parallelism. The algorithm presented in this paper overcomes some of the limitations of existing algorithms such as theα-algorithm (e.g., short-loops) and therefore enhances the applicability of process mining.