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Discovering High-Level Process Models from Event Logs

https://doi.org/10.18255/1818-1015-2017-2-125-140

Abstract

Process mining is a relatively new field of computer science, which deals with process discovery and analysis based on event logs. In this paper we consider the problem of discovering a high-level business process model from a low-level event log, i.e. automatic synthesis of process models based on the information stored in event logs of information systems. Events in a high-level model are abstract events, which can be refined to low-level subprocesses, whose behavior is recorded in event logs. Models synthesis is intensively studied in the frame of process mining research, but only models and event logs of the same granularity are mainly considered in the literature. Here we present an algorithm for discovering high-level acyclic process models from event logs and some specified partition of low-level events into subsets associated with abstract events in a high-level model.

About the Authors

Antonina A. Begicheva
National Research University Higher School of Economics Laboratory of Process-Aware Information Systems
Russian Federation

research assistant

20 Myasnitskaya str., Moscow 101000, Russia



Irina A. Lomazov
National Research University Higher School of Economics
Russian Federation

Doctor of science, professor

20 Myasnitskaya str., Moscow 101000, Russia



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Review

For citations:


Begicheva A.A., Lomazov I.A. Discovering High-Level Process Models from Event Logs. Modeling and Analysis of Information Systems. 2017;24(2):125-140. https://doi.org/10.18255/1818-1015-2017-2-125-140

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ISSN 1818-1015 (Print)
ISSN 2313-5417 (Online)