Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31529
Object-Centric Process Mining Using Process Cubes

Authors: Anahita Farhang Ghahfarokhi, Alessandro Berti, Wil M.P. van der Aalst

Abstract:

Process mining provides ways to analyze business processes. Common process mining techniques consider the process as a whole. However, in real-life business processes, different behaviors exist that make the overall process too complex to interpret. Process comparison is a branch of process mining that isolates different behaviors of the process from each other by using process cubes. Process cubes organize event data using different dimensions. Each cell contains a set of events that can be used as an input to apply process mining techniques. Existing work on process cubes assume single case notions. However, in real processes, several case notions (e.g., order, item, package, etc.) are intertwined. Object-centric process mining is a new branch of process mining addressing multiple case notions in a process. To make a bridge between object-centric process mining and process comparison, we propose a process cube framework, which supports process cube operations such as slice and dice on object-centric event logs. To facilitate the comparison, the framework is integrated with several object-centric process discovery approaches.

Keywords: Process mining, multidimensional process mining, multi-perspective business processes, OLAP, process cubes, process discovery.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 519

References:


[1] A. Bolt and W. van der Aalst, “Multidimensional process mining using process cubes,” in Enterprise, Business-Process and Information Systems Modeling. Springer, 2015, pp. 102–116.
[2] J. Ribeiro and A. Weijters, “Event cube: another perspective on business processes,” in International Conferences On the Move to Meaningful Internet Systems. Springer, 2011, pp. 274–283.
[3] C. Chen, X. Yan, F. Zhu, J. Han, and S. Y. Philip, “Graph OLAP: a multi-dimensional framework for graph data analysis,” Knowledge and information systems, vol. 21, no. 1, pp. 41–63, 2009.
[4] W. van der Aalst, “Object-centric process mining: Dealing with divergence and convergence in event data,” in International Conference on Software Engineering and Formal Methods. Springer, 2019, pp. 3–25.
[5] T. Vogelgesang and H.-J. Appelrath, “Multidimensional process mining: a flexible analysis approach for health services research,” in Proceedings of the Joint EDBT/ICDT 2013 Workshops, 2013, pp. 17–22.
[6] W. van der Aalst, “Process cubes: Slicing, dicing, rolling up and drilling down event data for process mining,” in Asia-Pacific Conference on Business Process Management. Springer, 2013, pp. 1–22.
[7] R. Andreswari and M. A. Rasyidi, “OLAP cube processing of production planning real-life event log: A case study,” in ICoIESE 2018. Atlantis Press, 2019.
[8] M. R. H. Nik, W. van der Aalst, and M. F. Sani, “Bipm: Combining bi and process mining.” in DATA, 2019, pp. 123–128.
[9] D. Cohn and R. Hull, “Business artifacts: A data-centric approach to modeling business operations and processes.” IEEE Data Eng. Bull., vol. 32, no. 3, pp. 3–9, 2009.
[10] K. Bhattacharya, C. Gerede, R. Hull, R. Liu, and J. Su, “Towards formal analysis of artifact-centric business process models,” in International Conference on Business Process Management. Springer, 2007, pp. 288–304.
[11] X. Lu, M. Nagelkerke, D. van de Wiel, and D. Fahland, “Discovering interacting artifacts from ERP systems (extended version),” BPM reports, vol. 1508, 2015.
[12] G. Li, R. M. de Carvalho, and W. van der Aalst, “Automatic discovery of object-centric behavioral constraint models,” in International Conference on Business Information Systems. Springer, 2017, pp. 43–58.
[13] A. Berti and W. van der Aalst, “Extracting multiple viewpoint models from relational databases,” in Data-Driven Process Discovery and Analysis. Springer, 2018, pp. 24–51.
[14] W. van der Aalst and A. Berti, “Discovering Object-centric Petri nets,” in Fundamenta Informaticae, 2020.
[15] M. Gupta and A. Sureka, “Process cube for software defect resolution,” in Asia-Pacific Software Engineering Conference, vol. 1. IEEE, 2014, pp. 239–246.