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Discovery of High-Level Process Models Through Event Log Transformation

Student: Velichko Semyon

Supervisor: Irina A. Lomazova

Faculty: Faculty of Computer Science

Educational Programme: System and Software Engineering (Master)

Year of Graduation: 2016

Event logs, obtained as a result of work of business processes, can be used for different purposes, including for construction of the corresponding process models. These models can be consequently used in order to analyze and possibly improve the business processes. However, since very often event logs, obtained with help of information technologies, contain too big amount of redundant technical information, process models that are discovered from these logs may turn out to be too huge and hard to understand. It may happen because for modern information systems it is not a problem to keep all the details about process execution in a log. And since these systems cannot define themselves, which information is really important for the experts, they usually save all the low-level details about process execution in the log. In this work, an approach is proposed which can help to discover more compact and easy to understand process models, since these models are built on a higher level of abstraction from low-level details. It consists of two steps. On the first step, it transforms the event log to a more high-level one using information about the correspondence between activities of different levels of abstraction. Then, on the second step, it applies the modified process model discovery algorithm to the transformed log in order to build an appropriate process model.

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