摘要
传统过程挖掘算法是针对静态模型和静态日志进行设计的,不能直接用于演化过程的发现。为此,提出了一种过程挖掘算法,应用滑窗机制实现增量式算法设计,利用日志事件关系模型,引入日志事件关系计数和阈值机制,实现对事件日志流的持续挖掘,因而能够发现模型演化的历史及模型当前实际执行情况。分析了算法性质及相关参数的影响,并进行了实验验证。
Most existing process mining algorithms were designed for static models and static event logs, so they could not be used in mining evolutionary processes. To deal with this problem, an incremental mining algorithm was proposed, which applied a sliding window to event log stream. And event-relation count and event-relation threshold mechanism were introduced by applying log event-relation model. The unremitting mining of event log flow was realized and a series of models corresponding to evolutionary event logs were obtained. Algorithm property and relevant parameters effect were also analyzed. Experiments were performed to validate the proposed algorithm.
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2008年第1期203-208,共6页
Computer Integrated Manufacturing Systems
基金
国家自然科学基金资助项目(60373011)
国家973计划资助项目(2002CB312006)~~
关键词
过程挖掘
演化过程
滑窗算法
process mining
evolution process
sliding window algorithm
