Real-life processes are typically less structured and more complex than expected by stakeholders. For this reason, process discovery techniques often deliver models less understandable and useful than expected. In order to address this issue, we propose a method based on statistical inference for pre-processing event logs. We measure the distance between different segments of the event log, computing the probability distribution of observing activities in specific positions. Because segments are generated based on time-domain, business rules or business management system properties, we get a characterisation of these segments in terms of both business and process aspects. We demonstrate the applicability of this approach by developing a case study with real-life event logs and showing that our method is offering interesting properties in term of computational complexity.

Toward a new generation of log pre-processing methods for process mining / P. Ceravolo, E. Damiani, M. Torabi, S. Barbon (LECTURE NOTES IN BUSINESS INFORMATION PROCESSING). - In: Business Process Management Forum / [a cura di] J. Carmona, G. Engels, A. Kumar. - [s.l] : Springer Verlag, 2017. - ISBN 9783319650142. - pp. 55-70 (( Intervento presentato al 15. convegno International Conference on Business Process Management tenutosi a Barcelona nel 2017 [10.1007/978-3-319-65015-9_4].

Toward a new generation of log pre-processing methods for process mining

P. Ceravolo
Primo
;
E. Damiani
Secondo
;
2017

Abstract

Real-life processes are typically less structured and more complex than expected by stakeholders. For this reason, process discovery techniques often deliver models less understandable and useful than expected. In order to address this issue, we propose a method based on statistical inference for pre-processing event logs. We measure the distance between different segments of the event log, computing the probability distribution of observing activities in specific positions. Because segments are generated based on time-domain, business rules or business management system properties, we get a characterisation of these segments in terms of both business and process aspects. We demonstrate the applicability of this approach by developing a case study with real-life event logs and showing that our method is offering interesting properties in term of computational complexity.
Event-log clustering; Lightweight trace profiling; Pre-processing; Process mining; Management Information Systems; Control and Systems Engineering; Business and International Management; Information Systems; Modeling and Simulation; Information Systems and Management
Settore INF/01 - Informatica
2017
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/527760
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