Process mining uses business event logs to understand the flow of activities, to identify anomalous cases and to enhance processes. Today, real-time process mining tools mainly deal with a single task at a time (process discovery, conformance checking, process enhancement or concept change detection). In this paper, we introduce an underlined layer overlapping with multiple online process mining tasks to smooth their integration. Following a case clustering approach, based on trace and time analysis, our proposal supports simultaneously: process discovery, conformance checking, and concept drift detection. We evaluated our approach and compared it with other techniques using both real-life and synthetic data, obtaining promising results.
Overlapping Analytic Stages in Online Process Mining / G.M. Tavares, P. Ceravolo, V.G. Turrisi Da Costa, E. Damiani, S. Barbon Junior - In: 2019 IEEE International Conference on Services Computing (SCC)[s.l] : IEEE, 2019 Aug. - ISBN 9781728127200. - pp. 167-175 (( convegno International Conference on Services Computing tenutosi a Milano nel 2019 [10.1109/SCC.2019.00037].
Overlapping Analytic Stages in Online Process Mining
G.M. Tavares;P. Ceravolo
;E. Damiani;
2019
Abstract
Process mining uses business event logs to understand the flow of activities, to identify anomalous cases and to enhance processes. Today, real-time process mining tools mainly deal with a single task at a time (process discovery, conformance checking, process enhancement or concept change detection). In this paper, we introduce an underlined layer overlapping with multiple online process mining tasks to smooth their integration. Following a case clustering approach, based on trace and time analysis, our proposal supports simultaneously: process discovery, conformance checking, and concept drift detection. We evaluated our approach and compared it with other techniques using both real-life and synthetic data, obtaining promising results.File | Dimensione | Formato | |
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