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Using multi-target feature evaluation to discover factors that affect business process behavior

Delias, Pavlos; Lagopoulos, Athanasios; Tsoumakas, Grigorios; Grigori, Daniela (2018), Using multi-target feature evaluation to discover factors that affect business process behavior, Computers in Industry, 99, p. 253-261. 10.1016/j.compind.2018.03.022

Type
Article accepté pour publication ou publié
Date
2018
Journal name
Computers in Industry
Volume
99
Publisher
Elsevier
Pages
253-261
Publication identifier
10.1016/j.compind.2018.03.022
Metadata
Show full item record
Author(s)
Delias, Pavlos
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Lagopoulos, Athanasios
University of Thessaloniki
Tsoumakas, Grigorios
University of Thessaloniki
Grigori, Daniela
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Abstract (EN)
Certain business environments, like health-care or customer service, host complex and highly variable business processes. In such situations, we expect fluctuating process behavior, which is difficult to attribute to specific causes, at least automatically. This work aims to provide process analysts with an additional tool to discover factors that affect the process flow. To this end, we propose a three-stage methodology to deal with the several challenges of this goal.Adhering to the process mining paradigm that suggests for evidence-based process analysis and improvement, we introduce a horizontal partitioning approach to identify elements of process behavior during the first stage. Then, during the second stage, we discuss how log manipulations can yield characteristics that reflect various perspectives of the process. Finally, we propose a multi-target feature evaluation step to deliver insights about the associations between characteristics and process behavior.The proposed methodology is designed to tackle challenges related to the general correlation problem of process mining, like dealing with general process behavior (not just local decisions) and relaxing the independence assumption among the elements of behavior. We demonstrate our approach step by step through a case study on a real-world, open dataset.
Subjects / Keywords
Process mining; General correlation problem; Multi-target prediction

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