Detail publikace

Use of Frequent Itemset Mining Techniques to Analyze Business Processes

Originální název

Use of Frequent Itemset Mining Techniques to Analyze Business Processes

Anglický název

Use of Frequent Itemset Mining Techniques to Analyze Business Processes

Jazyk

en

Originální abstrakt

Analysis of business process data can be used to discover reasons of delays and other problems of 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.

Anglický abstrakt

Analysis of business process data can be used to discover reasons of delays and other problems of 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.

BibTex


@inproceedings{BUT119870,
  author="Vladimír {Bartík} and Milan {Pospíšil}",
  title="Use of Frequent Itemset Mining Techniques to Analyze Business Processes",
  annote="Analysis of business process data can be used to discover reasons of delays and
other problems of 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.",
  address="SciTePress - Science and Technology Publications",
  booktitle="Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management",
  chapter="119870",
  edition="NEUVEDEN",
  howpublished="print",
  institution="SciTePress - Science and Technology Publications",
  year="2015",
  month="november",
  pages="273--280",
  publisher="SciTePress - Science and Technology Publications",
  type="conference paper"
}