Quality assessment of discovered process models in Process Mining: the case of Process Trees

Cristina-Claudia Osman

Abstract


Daily activities of companies generate and consume massive amounts of data. Different diagrammatic visualizations can be extracted from this data by using different Process Mining algorithms. ProM Framework provides several discovery Process Mining algorithms, mainly focused on the control-flow perspective. This paper analyses the algorithms whose output is either a Process Tree (PT), or an Efficient Process Tree (EPT). The results of several Process Mining algorithms are analyzed and qualitatively evaluated. Precision, Scaled Precision, and Fitness metrics are used for evaluating the resulted diagrammatic visualizations. Moreover, two variations of F-score are also introduced for determining the global quality of the models. The analysis considers, on one hand, two algorithms whose output is a PT and, on the other hand, five versions of an algorithm whose output is an EPT. The findings of this investigation show slightly better results on EPT compared to PT. However, the choice of the most suitable algorithm depends on the analysis type (process discovery, process improvement, audit, risk identification, etc.).


Full Text:

PDF

References


W. M. P van der Aalst, Process Mining: Data science in action, Springer, Berlin, Heidelberg, 2016.

W. M. P. van der Aalst, T. Weijters, and L. Maruster, “Workflow Mining: Discovering Process Models from Event Logs,” IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 9, pp. 1128-1142, 2004.

L. Wen, J. Wang, W. M. P. van der Aalst, B. Huan, and J. Sun, “Mining process models with prime invisible tasks,” Data & Knowledge Engineering, vol. 69, no. 10, pp. 999- 1021, 2010.

S. J. J. Leemans, D. Fahland, and W. M .P. van der Aalst, “Process and deviation exploration with Inductive visual Miner,” in CEUR Workshop Proceedings of the BPM Demo Sessions 2014 Co-located with the 12th International Conference on Business Process Management, vol. 1295, Eindhoven, 2014, pp. 46-50.

J. M. E. Van der Werf, B. F. van Dongen, C .A. Hurkens, and A. Serebrenik, “Process discovery using integer linear programming,” in Applications and Theory of Petri Nets. PETRI NETS 2008. Lecture Notes in Computer Science, vol. 5062, Springer, Berlin, Heidelberg, Xi’an, 2008, pp. 368-387.

A. J. M. M. Weijters, J. T. S. Ribeiro, “Flexible Heuristics Miner (FHM),” BETA Working Paper Series, WP 334, Eindhoven University of Technology, Eindhoven, 2010.

R. Conforti, M. Dumas, L. García-Bañuelos, and M. La Rosa, “BPMN Miner: Automated discovery of BPMN process models with hierarchical structure” Information Systems, 56, pp. 284-303, 2016.

C. W. Günther, and W. M. P. van der Aalst, “Fuzzy mining–adaptive process simplification based on multi-perspective metrics,” in Business Process Management. BPM 2007. Lecture Notes in Computer Science, vol. 4714, 2007, Springer, Berlin, Heidelberg, Brisbane, pp. 328-343.

W. M. P van der Aalst, H. A. Reijers, and M. Song, “Discovering social networks from event logs,” Computer Supported Cooperative Work (CSCW) vol. 14, no. 6, pp. 549-593, 2005.

M. Song, and W .M. P. van der Aalst, “Towards comprehensive support for organizational mining,” Decision Support Systems, vol. 46, no. 1, pp. 300-317, 2008.

J. M. E. M. van der Werf, B.F. van Dongen, C. A. J. Hurkens, and A. Serebrenik, “Process Discovery using Integer Linear Programming,” Fundamenta Informaticae, vol. 94, no. 3-4, pp. 387-412, 2010.

F. Mannhardt, M. de Leoni, H. A. Reijers, W. M. P. van der Aalst, “Data-driven Process Discovery - Revealing Conditional Infrequent Behavior From Event Logs,” in Advanced Information Systems Engineering. CAiSE 2017. Lecture Notes in Computer Science, vol. 10253, Springer, Cham, 2017, Essen, pp. 545–560.

R. Petrușel, I. Vanderfeesten, C. C. Dolean, and D. Mican, “Making decision process knowledge explicit using the decision data model,” in Business Information Systems. BIS 2011. Lecture Notes in Business Information Processing, vol. 87, 2011, Springer, Berlin, Heidelberg, pp. 172-184.

C.-C. Dolean, “Mining Product Data Models: A Case Study,” Informatica Economica, vol. 18, no. 1, pp. 69-82, 2014.

S. J. J. Leemans, N. Tax, and A. H. M. ter Hofstede, “Indulpet miner: Combining discovery algorithms” in OTM 2018 Conferences. OTM 2018. Lecture Notes in Computer Science, vol. 11229, 2018, Springer, Cham, Valletta, pp. 97-115.

M. L. van Eck, J. C. A. M. Buijs, and B. F. van Dongen, “Genetic process mining: Alignment-based process model mutation,” in International Conference on Business Process Management, Business Process Management Workshops. BPM 2014. Lecture Notes in Business Information Processing, vol. 202, 2014, Springer, Cham, Eindhoven, pp. 291-303.

J.C.A.M. Buijs, “Flexible evolutionary algorithms for mining structured process models,” Ph.D. Thesis, Eindhoven University of Technology, Netherland, 2014.

S. J. J. Leemans, “Robust process mining with guarantees,” Ph.D. thesis, Eindhoven University of Technology, Netherlands, 2017.

N. Tax, N. Sidorova, R. Haakma, and W. M. P. van der Aalst, “Mining local process models,” Journal of Innovation in Digital Ecosystems, vol. 3, no. 2, pp. 183-196, 2016.

J. C. A. M. Buijs, B. F. van Dongen, and W. M. P. van der Aalst, “A genetic algorithm for discovering process trees,” in IEEE Congress on Evolutionary Computation, Brisbane, pp. 1-8, 2012.

J. C. A. M. Buijs, B. F. van Dongen, and W. M. P. van der Aalst, “Mining Configurable Process Models from Collections of Event Logs,” in Business Process Management. Lecture Notes in Computer Science, vol. 8094, Springer, Berlin, Heidelberg, 2013, Beijing, pp. 33-48.

S. J. J. Leemans, D. Fahland, and W. M. P. van der Aalst, “Discovering block-structured process models from event logs containing infrequent behaviour,” in Business Process Management Workshops. BPM 2013. Lecture Notes in Business Information Processing, vol. 171, Springer, Cham, 2013, Beijing, pp. 66-78.

M. Weidlich, and J. M. van der Werf, “On profiles and footprints–relational semantics for petri nets,” in Application and Theory of Petri Nets. PETRI NETS 2012. Lecture Notes in Computer Science, vol. 7347, Springer, Berlin, Heidelberg, Xi’an, 2008, pp. 148-167.

S. J. J. Leemans, D. Fahland, and W. M. P. van der Aalst, “Using life cycle information in process discovery,” in Business Process Management Workshops. BPM 2016. Lecture Notes in Business Information Processing, vol. 256. Springer, Cham, Innsbruck, 2016, pp. 204-217.

J. C. A. M. Buijs, B. F. van Dongen, W. M. P. van der Aalst, “Quality dimensions in process discovery: The importance of fitness, precision, generalization and simplicity,” in International Journal of Cooperative Information Systems, vol. 23, no. 1, pp. 1-39, 2014.

C.-C. Osman, “Quality assessment of process models in Process Mining: the case of Petri Nets,” in Proceedings of 18th International Conference on INFORMATICS in ECONOMY. Education, Research and Business Technologies, Bucharest, 2019, pp.199-205.

A. Djedović, “Electronic Invoicing Event Logs,” 4TU, Centre for Research Data, https://doi.org/10.4121/uuid:5a9039b8-794a-4ccd-a5ef-4671f0a258a4.

J. De Weerdt, M. De Backer, J. Vanthienen, and B. Baesens, “A robust F-measure for evaluating discovered process models,” in 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), Paris, 2011, pp. 148-155.

S. J. J. Leemans, D. Fahland, and W. M. P. van der Aalst, “Scalable process discovery and conformance checking,” Software & Systems Modeling, vol. 17, no. 2, pp. 599-631, 2018.

H. M. W. Verbeek, J. C. A. M. Buijs, B. F. van Dongen, W. M. P. van der Aalst, “Prom 6: The process mining toolkit,,” in BPM 2010 Demonstration Track, New Jersey, 2010, pp. 34-39.

C. J. van Rijsbergen, Information Retrieval, Butterworth, 1979.

W. M. P. van der Aalst, A. Adriansyah, and B. F. van Dongen, “Replaying history on process models for conformance checking and performance analysis,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 182-192, 2012.

C. W. Günther, and E. Verbeek, “XES Standard Definition,” https://xes-standard.org/_media/xes/xesstandarddefinition-2.0.pdf

S. J. J. Leemans, D. Fahland, and W. M. P. van der Aalst, “Exploring processes and deviations,” in Business Process Management Workshops. BPM 2014. Lecture Notes in Business Information Processing, vol. 202, Springer, Cham, Eindhoven, 2014, pp. 304-316.


Refbacks



Abava  Кибербезопасность MoNeTec 2024

ISSN: 2307-8162