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National Research University Higher School of EconomicsStudent ThesesAlgorithm for Abstract Business Process Model from a Low-Level Event Log

Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Antonina Begicheva
Algorithm for Abstract Business Process Model from a Low-Level Event Log
Software Engineering
(Bachelor’s programme)
2016
This paper is dedicated to discovering process as an abstract model from a low-level event log.

Process mining is a relatively new field of computer science, which deals with process discovery and analysis based on event logs. When working with business processes we typically use detailed logs, which present the full report about sequences of executed activities. Experts prefer working with more abstract (high-level) models. More abstract models are easier to construct, understand and analyze.

In this paper we consider the problem of business process model discovering. Model’s synthesis is intensively studied in the frame of process mining research. In the literature were considered only models and event logs of the same granularity. They provide no or only limited support for correctly refining these mappings based on domain knowledge. Here we present a solution for high-level business process model based on the theory of regions that allows us to get immediately a high-level model without intermediate stages.

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