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A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs

A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs
A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs
Process mining is the research domain that is dedicated to the a posteriori analysis of business process executions. The techniques developed within this research area are specifically designed to provide profound insight by exploiting the untapped reservoir of knowledge that resides within event logs of information systems. Process discovery is one specific subdomain of process mining that entails the discovery of control-flow models from such event logs. Assessing the quality of discovered process models is an essential element, both for conducting process mining research as well as for the use of process mining in practice. In this paper, a multi-dimensional quality assessment is presented in order to comprehensively evaluate process discovery techniques. In contrast to previous studies, the major contribution of this paper is the use of eight real-life event logs. For instance, we show that evaluation based on real-life event logs significantly differs from the traditional approach to assess process discovery techniques using artificial event logs. In addition, we provide an extensive overview of available process discovery techniques and we describe how discovered process models can be assessed regarding both accuracy and comprehensibility. The results of our study indicate that the HeuristicsMiner algorithm is especially suited in a real-life setting. However, it is also shown that, particularly for highly complex event logs, knowledge discovery from such data sets can become a major problem for traditional process discovery techniques.
process mining, benchmarking, real-life eventlogs, accuracy, comprehensibility
0306-4379
De Weerdt, Jochen
1eaa177f-03d0-47e5-b8b6-4fb419d49e47
De Backer, Manu
9c56870f-a34a-4eba-87ef-137fec532349
Vanthienen, Jan
6f3d818f-0fce-46fa-966b-160e645caf6d
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
De Weerdt, Jochen
1eaa177f-03d0-47e5-b8b6-4fb419d49e47
De Backer, Manu
9c56870f-a34a-4eba-87ef-137fec532349
Vanthienen, Jan
6f3d818f-0fce-46fa-966b-160e645caf6d
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0

De Weerdt, Jochen, De Backer, Manu, Vanthienen, Jan and Baesens, Bart (2012) A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs. Information Systems. (doi:10.1016/j.is.2012.02.004).

Record type: Article

Abstract

Process mining is the research domain that is dedicated to the a posteriori analysis of business process executions. The techniques developed within this research area are specifically designed to provide profound insight by exploiting the untapped reservoir of knowledge that resides within event logs of information systems. Process discovery is one specific subdomain of process mining that entails the discovery of control-flow models from such event logs. Assessing the quality of discovered process models is an essential element, both for conducting process mining research as well as for the use of process mining in practice. In this paper, a multi-dimensional quality assessment is presented in order to comprehensively evaluate process discovery techniques. In contrast to previous studies, the major contribution of this paper is the use of eight real-life event logs. For instance, we show that evaluation based on real-life event logs significantly differs from the traditional approach to assess process discovery techniques using artificial event logs. In addition, we provide an extensive overview of available process discovery techniques and we describe how discovered process models can be assessed regarding both accuracy and comprehensibility. The results of our study indicate that the HeuristicsMiner algorithm is especially suited in a real-life setting. However, it is also shown that, particularly for highly complex event logs, knowledge discovery from such data sets can become a major problem for traditional process discovery techniques.

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More information

Accepted/In Press date: 2012
e-pub ahead of print date: 5 March 2012
Keywords: process mining, benchmarking, real-life eventlogs, accuracy, comprehensibility
Organisations: Southampton Business School

Identifiers

Local EPrints ID: 336463
URI: http://eprints.soton.ac.uk/id/eprint/336463
ISSN: 0306-4379
PURE UUID: 8a1a14d1-8e28-483b-ae57-d3d100992405
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 27 Mar 2012 10:23
Last modified: 10 Dec 2019 01:45

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