Publikation

Titel:

„ILP² Miner – Process Discovery for Partially Ordered Event Logs Using Integer Linear Programming“

AutorInnen:

Sabine Folz-Weinstein
Robin Bergenthum
Jörg Desel
Jakub Kovář

Kategorie:

Konferenzbandbeiträge

erschienen in:

Proceedings of PETRI NETS 2023, Lecture Notes in Computer Science 13929: 59-76 (2023)

Abstract:

Process mining is based on event logs. Traditionally, an event log is a sequence of events. Yet, there is a growing amount of work in the literature that suggests we should extend the notion of an event log and use partially ordered logs as a basis for process mining. Thus, the need for algorithms able to handle these partially ordered logs will grow in the upcoming years. In this paper, we adapt an existing, classical process discovery algorithm to be able to handle partially ordered logs. We use the ILP Miner as a basis and replace its region theory part by compact tokenflow regions to introduce the ILP² Miner. This ILP² Miner handles sequential event logs just like the ILP Miner but, in addition, is able to directly process partially ordered logs. We prove that the ILP² Miner provides the same guarantees regarding structural and behavioral properties of the discovered process models as the ILP Miner. We implement the ILP² Miner and show experimental results of its runtime using three well-known example log files from the process mining community literature.

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