Dealing With Concept Drifts in Process Mining

被引：120

作者：

Bose, R. P. Jagadeesh Chandra ^{[1
]}

van der Aalst, Wil M. P. ^{[1
]}

Zliobaite, Indre ^{[2
]}

Pechenizkiy, Mykola ^{[1
]}

机构：

[1] Eindhoven Univ Technol, Dept Math & Comp Sci, NL-5600 MB Eindhoven, Netherlands

[2] Aalto Univ, Dept Informat & Comp Sci, FI-00076 Aalto, Finland

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2014年 / 25卷 / 01期

关键词：

Concept drift; flexibility; hypothesis tests; process changes; process mining; CLASSIFIERS; INFORMATION; MODELS;

D O I：

10.1109/TNNLS.2013.2278313

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Although most business processes change over time, contemporary process mining techniques tend to analyze these processes as if they are in a steady state. Processes may change suddenly or gradually. The drift may be periodic (e.g., because of seasonal influences) or one-of-a-kind (e.g., the effects of new legislation). For the process management, it is crucial to discover and understand such concept drifts in processes. This paper presents a generic framework and specific techniques to detect when a process changes and to localize the parts of the process that have changed. Different features are proposed to characterize relationships among activities. These features are used to discover differences between successive populations. The approach has been implemented as a plug-in of the ProM process mining framework and has been evaluated using both simulated event data exhibiting controlled concept drifts and real-life event data from a Dutch municipality.

引用

页码：154 / 171

页数：18

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