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'Learning high-level process models from event data', Doctor of Philosophy, Department of Mathematics and Computer Science

Information systems in different domains, such as healthcare, tourism, banking, government and others, record operational behavior in the form of event logs. The process mining discipline offers dozens of techniques to discover, analyze, and visualize processes running in information systems, based on their event logs. The representational bias (the language for processes representation) plays an important role in the process discovery. In this work BPMN (Business Process Model and Notation) language was chosen as a representational bias and as a starting point for the process discovery, analysis and enhancement. BPMN is a common process modeling language, widely used by consultants, managers, analysts, and software engineers in various application domains. This work aims to bridge the gap between process mining techniques and BPMN. Existing techniques are often limited to a single perspective, e.g., just the control flow, subprocesses, or just resources. The goal of this work is to fully support the BPMN specification in the context of process mining and suggest a unified and integrated approach allowing for the discovery, analysis and enhancement of hierarchical high-level BPMN models. The approach proposed in this thesis is supported by tools that enable users to analyze discovered processes in BPMN-compliant tools and even automate their executions, using existing BPMN engines.

'Learning high-level process models from event data', Doctor of Philosophy, Department of Mathematics and Computer Science