Object-Centric Process Mining Using Process Cubes

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.

Authors:

• Anahita Farhang Ghahfarokhi
• Alessandro Berti
• Wil M.P. van der Aalst

Keywords:

• Process mining
• multidimensional process mining
• multi-perspective business processes
• OLAP
• process cubes
• process discovery.

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.