发明名称 |
DETECTING DEVIATIONS BETWEEN EVENT LOG AND PROCESS MODEL |
摘要 |
A method for detecting deviations between an event log and a process model includes converting the process model into a probability process model, the probability process model comprising multiple nodes in multiple hierarchies and probability distribution associated with the multiple nodes, a leaf node among the multiple nodes corresponding to an activity in the process model; detecting differences between at least one event sequence contained in the event log and the probability process model according to a correspondence relationship; and identifying the differences as the deviations in response to the differences exceeding a predefined threshold; wherein the correspondence relationship describes a correspondence relationship between an event in one event sequence of the at least one event sequence and a leaf node in the probability process model. |
申请公布号 |
US2015213373(A1) |
申请公布日期 |
2015.07.30 |
申请号 |
US201514598655 |
申请日期 |
2015.01.16 |
申请人 |
International Business Machines Corporation |
发明人 |
Li Jing;Li Xiang;Liu Haifeng;Xie Guo Tong;Yu Yi Qin;Zhang Shi Lei |
分类号 |
G06N7/00 |
主分类号 |
G06N7/00 |
代理机构 |
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代理人 |
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主权项 |
1. A method for detecting deviations between an event log and a process model, comprising:
converting, with a processing device, the process model into a probability process model, the probability process model comprising multiple nodes in multiple hierarchies and probability distribution associated with the multiple nodes, a leaf node among the multiple nodes corresponding to an activity in the process model; detecting differences between at least one event sequence contained in the event log and the probability process model according to a correspondence relationship; and identifying the differences as the deviations in response to the differences exceeding a predefined threshold; wherein the correspondence relationship describes a correspondence relationship between an event in one event sequence of the at least one event sequence and a leaf node in the probability process model. |
地址 |
Armonk NY US |