DocumentCode :
3605109
Title :
Discovering Interacting Artifacts from ERP Systems
Author :
Xixi Lu ; Nagelkerke, Marijn ; van de Wiel, Dennis ; Fahland, Dirk
Author_Institution :
Dept. of Math. & Comput. Sci., Eindhoven Univ. of Technol., Eindhoven, Netherlands
Volume :
8
Issue :
6
fYear :
2015
Firstpage :
861
Lastpage :
873
Abstract :
Enterprise Resource Planning (ERP) systems are widely used to manage business documents along a business processes and allow very detailed recording of event data of past process executions and involved documents. This recorded event data is the basis for auditing and detecting unusual flows. Process mining techniques can analyze event data of processes stored in linear event logs to discover a process model that reveals unusual executions. Existing approaches to obtain linear event logs from ERP data require a single case identifier to which all behavior can be related. However, in ERP systems processes such as Order to Cash operate on multiple interrelated business objects, each having their own case identifier, their own behavior, and interact with each other. Forcing these into a single case creates ambiguous dependencies caused by data convergence and divergence which obscures unusual flows in the resulting process model. In this paper, we present a new semi-automatic, end-to-end approach for analyzing event data in a plain database of an ERP system for unusual executions. More precisely, we identify an artifact-centric process model describing the business objects, their life-cycles, and how the various objects interact along their life-cycles. This way, we prevent data divergence and convergence. We report on two case studies where our approach allowed to successfully analyze processes of ERP systems and reliably revealed unusual flows later confirmed by domain experts.
Keywords :
data analysis; data mining; document handling; enterprise resource planning; ERP systems; artifact-centric process model; business document management; data convergence; enterprise resource planning; event data analysis; interacting artifact discovery; life-cycles; linear event logs; process mining; unusual flow auditing; unusual flow detection; Analytical models; Convergence; Data mining; Data models; Object oriented modeling; Object recognition; Artifact-Centric Processes; ERP Systems; ERP systems; Log Conversion; Outlier Detection; Process Discovery; Process discovery; Relational Data; artifact-centric processes; log conversion; outlier detection; relational data;
fLanguage :
English
Journal_Title :
Services Computing, IEEE Transactions on
Publisher :
ieee
ISSN :
1939-1374
Type :
jour
DOI :
10.1109/TSC.2015.2474358
Filename :
7229358
Link To Document :
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