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Open AccessArticle

Analysis of SAP Log Data Based on Network Community Decomposition

by Martin Kopka 1,2,* and Miloš Kudělka 2
1
Consulting 4U, 779 00 Olomouc, Czech Republic
2
Department of Computer Science, VSB—Technical University of Ostrava, 708 00 Ostrava-Poruba, Czech Republic
*
Author to whom correspondence should be addressed.
Information 2019, 10(3), 92; https://doi.org/10.3390/info10030092
Received: 27 January 2019 / Revised: 18 February 2019 / Accepted: 25 February 2019 / Published: 1 March 2019
(This article belongs to the Special Issue Computational Social Science)
Information systems support and ensure the practical running of the most critical business processes. There exists (or can be reconstructed) a record (log) of the process running in the information system. Computer methods of data mining can be used for analysis of process data utilizing support techniques of machine learning and a complex network analysis. The analysis is usually provided based on quantitative parameters of the running process of the information system. It is not so usual to analyze behavior of the participants of the running process from the process log. Here, we show how data and process mining methods can be used for analyzing the running process and how participants behavior can be analyzed from the process log using network (community or cluster) analyses in the constructed complex network from the SAP business process log. This approach constructs a complex network from the process log in a given context and then finds communities or patterns in this network. Found communities or patterns are analyzed using knowledge of the business process and the environment in which the process operates. The results demonstrate the possibility to cover up not only the quantitative but also the qualitative relations (e.g., hidden behavior of participants) using the process log and specific knowledge of the business case. View Full-Text
Keywords: decision support; process log data; network construction; visualization (visual data mining); community detection (network clustering); pattern and outlier analysis; recursive procedure (cluster quality) decision support; process log data; network construction; visualization (visual data mining); community detection (network clustering); pattern and outlier analysis; recursive procedure (cluster quality)
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Kopka, M.; Kudělka, M. Analysis of SAP Log Data Based on Network Community Decomposition. Information 2019, 10, 92.

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