Process concept drift detection approach based on log completeness

被引：0

作者：

Lin L. ^{[1
,2
]}

Wen L. ^{[2
]}

Zhou H. ^{[3
]}

Pei J. ^{[2
]}

Dai F. ^{[3
]}

Zheng C. ^{[2
]}

机构：

[1] School of Software, Yunnan University, Kunming

[2] School of Software, Tsinghua University, Beijing

[3] School of big data and Intelligence Engineering, Southwest Forestry University, Kunming

来源：

Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2019年 / 25卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Business process management; Chebyshe'v inequality; Drift detection; Log completeness; Process mining;

D O I：

10.13196/j.cims.2019.04.009

中图分类号：

学科分类号：

摘要：

The purpose of is to assert whether the model has changed by detecting changes in the given log. However, Aiming at the limitations of existing drift detection methods in process mining that were large feature extraction, detection delay and inability to locate changing region accurately, a novel method for sudden drift detection was proposed. The problem of sudden drift detection was transformed into local completeness computing problem of the given log. Then Chebyshev inequality was used to deduce the expression of local completeness based on direct succession and frequency. To avoid the interference of exclusiveness and concurrency, a training window was adopted to calculate the initial value of local completeness. In addition, the cutting operation was defined to ensure that the algorithm could run iteratively until all traces in log had been detected. Experiments on synthetic logs showed that the proposed method was effective in detecting sudden drift. © 2019, Editorial Department of CIMS. All right reserved.

引用

页码：873 / 881

页数：8

共 19 条

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