Publication
Title
Process discovery in event logs: an application in the telecom industry
Author
Abstract
The abundant availability of data is typical for information-intensive organizations. Usually, discerning knowledge from vast amounts of data is a challenge. Similarly, discovering business process models from information system event logs is definitely non-trivial. Within the analysis of event logs, process discovery, which can be defined as the automated construction of structured process models from such event logs, is an important learning task. However, the discovery of these processes poses many challenges. First of all, human-centric processes are likely to contain a lot of noise as people deviate from standard procedures. Other challenges are the discovery of so-called non-local, non-free choice constructs, duplicate activities, incomplete event logs and the inclusion of prior knowledge. In this paper, we present an empirical evaluation of three state-of-the-art process discovery techniques: Genetic Miner, AGNEs and HeuristicsMiner. Although the detailed empirical evaluation is the main contribution of this paper to the literature, an in-depth discussion of a number of different evaluation metrics for process discovery techniques and a thorough discussion of the validity issue are key contributions as well.
Language
English
Source (journal)
Applied soft computing. - Place of publication unknown
Publication
Place of publication unknown : 2011
ISSN
1568-4946
Volume/pages
11:2(2011), p. 1697-1710
ISI
000286373200021
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
[E?say:metaLocaldata.cgzprojectinf]
Publication type
Subject
External links
Web of Science
Record
Identification
Creation 11.01.2011
Last edited 13.11.2017
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