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National Research University Higher School of EconomicsStudent ThesesMining Hierarchical Process Model from Event Logs

Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Anastasiya Kulakova
Mining Hierarchical Process Model from Event Logs
Applied Mathematics and Information Science
(Bachelor’s programme)
2017
This paper focuses on the discovering process as an abstract model from a low-level event log.

Modern Information Systems have a complex structure. A large amount of information is generated according to the Information Systems' processes. These information is recorded in Information Systems' event log. Process mining is technology, that allows to build model and study the structure of processes by extracting data from the event log to improve and optimize their functioning. The model of process, that directly build from the event log, is often very comprehensive to analyze, because it contains many different events. Therefore, experts prefer to work with simplified and more abstract models, presenting a groups of events, which is belong to the same subprocess, as one abstract event. However, existing algorithms of process mining for process models discovering are designed to work with single-level data and do not allow the user to specify a match between the events and their subprocesses to extract a compact model. The aim of this research is to develop an algorithm that allows to obtain an abstract process model from a detailed log, given the correspondence between the low-level events and the subprocesses to which they belong.

Keywords: process mining, abstract models, process models, low-level log, Petri nets, algorithm for bringing the detailed log to the abstract.

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