Use of frequent itemset mining techniques to analyze business processes

Abstract

Analysis of business process data can be used to discover reasons of delays and other problems in a business process. This paper presents an approach, which uses a simulator of production history. This simulator allows detecting problems at various production machines, e.g. extremely long queues of products waiting before a machine. After detection, data about products processed before the queue increased are collected. Frequent itemsets obtained from this dataset can be used to describe the problem and reasons of it. The whole process of frequent itemset mining will be described in this paper. It is also focused on description of several necessary modifications of basic methods usually used to discover frequent itemsets.