LCDD: Detecting Business Process Drifts Based on Local Completeness

Abstract

Flexibility and evolution have been a hot topic in the context of business process management. However, contemporary process mining techniques assume processes to be in a steady state, which will result in a mixed-model mined from the event log. Business process drift detection is a family of methods to detect changes by analyzing the event log, but existing methods have some disadvantages in dealing with concept drifts. First, most of these methods detect changes depending on an exploration of a potentially large feature space and are time consuming. Second, with the size of delay period becoming small, the accuracies of some methods will drop rapidly. In this article, we address these problems by proposing a novel drift detection technique called LCDD that significantly differs from existing methods. The core idea is to find out new features and disappeared features from traces after the log fragment meets local completeness. To begin with, we use the relations of direct succession as the lightweight feature, which are compared between two windows (complete window and detection window). Then, we find new direct successions and stable disappeared direct successions by just moving detection window. Finally, the forgetting mechanism is used to abandon some direct successions after finding a change point. An extensive empirical evaluation shows that LCDD is faster and more accurate.

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