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dc.contributor.authorKopka, Martin
dc.contributor.authorKudělka, Miloš
dc.date.accessioned2019-05-14T08:56:27Z
dc.date.available2019-05-14T08:56:27Z
dc.date.issued2019
dc.identifier.citationInformation. 2019, vol. 10, issue 3, art. no. 92.cs
dc.identifier.issn2078-2489
dc.identifier.urihttp://hdl.handle.net/10084/134921
dc.description.abstractInformation 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.cs
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofseriesInformationcs
dc.relation.urihttps://doi.org/10.3390/info10030092cs
dc.rights© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectdecision supportcs
dc.subjectprocess log datacs
dc.subjectnetwork constructioncs
dc.subjectvisualization (visual data mining)cs
dc.subjectcommunity detection (network clustering)cs
dc.subjectpattern and outlier analysiscs
dc.subjectrecursive procedure (cluster quality)cs
dc.titleAnalysis of SAP log data based on network community decompositioncs
dc.typearticlecs
dc.identifier.doi10.3390/info10030092
dc.rights.accessopenAccesscs
dc.type.versionpublishedVersioncs
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume10cs
dc.description.issue3cs
dc.description.firstpageart. no. 92cs
dc.identifier.wos000464294500001


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© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Except where otherwise noted, this item's license is described as © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.