evaluate supervised event abstraction results that fits closely to the tasks of process discovery and conformance checking. We conclude this paper by demonstrating the usefulness of supervised event abstraction for obtaining more structured and/or more comprehensible process models using both real life event data and synthetic event data.'
author:
- '\'
- '\'
bibliography:
- 'IEEEabrv.bib'
- 'bibliography.bib'
title: Event Abstraction for Process Mining using Supervised Learning Techniques
---

Process Mining, Event Abstraction, Probabilistic Graphical Models

Introduction
============

Process mining is a fast growing discipline that combines knowledge and techniques from computational intelligence, data mining, process modeling and process analysis [@Aalst2011]. Process mining focuses on the analysis of event logs, which consists of sequences of real-life events observed from process executions, originating e.g. from logs from ERP systems. An important subfield of process mining is process discovery, which is concerned with the task of finding a process model that is representative of the behavior seen in an event log. Many different process discovery algorithms exist ([@Aalst2004; @Gunther2007; @Werf2008; @Weijters2011; @Leemans2013]), and many different types of process models can be discovered by process discovery methods, including BPMN models, Petri nets, process trees, and statecharts.

![An excerpt of a “spaghetti”-like process model.[]{data-label="fig:spaghetti"}](spaghetti){width="48.00000%"}

