A Method of Adaptive Process Mining Based on Time-Varying sliding Window and Relation of Adjacent Event Dependency
Most existing process mining methods were designed for ignoring time variability from real business process data, thus it could be hard to implement adaptive process mining. To deal with this problem, a new method of adaptive process mining was proposed in order to mine unremittingly process models of gradual change which represents the improvement stages of business processes and improve accuracy of mined results. Given related concepts of a time-varying sliding window and relation of adjacent event dependency, update rules of modifying continuously size and progress in a time-varying sliding window were studied based on changed frequency of mined results and arrival rate of process instance streams, and an algorithm of process mining was presented by applying relation of adjacent event dependency among activities. Finally, a plug-in tool in PROM was developed to implement this algorithm.
Process Mining Time-varying Sliding Window Relation of Adjacent Event Dependency Adaptability
SHI Mei-hong JANG Shou-shan GUO Yong-gang CHEN Liang CAO Kai-duan
School of Computer Science,Xi’an Polytechnic University, Xi’an 710048, China
国际会议
三亚
英文
24-31
2012-01-06（万方平台首次上网日期，不代表论文的发表时间）