Scalable process discovery with guarantees

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

36 Citations (Scopus)
4 Downloads (Pure)

Abstract

Considerable amounts of data, including process event data, are collected and stored by organisations nowadays. Discovering a process model from recorded process event data is the aim of process discovery algorithms. Many techniques have been proposed, but none combines scalability with quality guarantees, e.g. can handle billions of events or thousands of activities, and produces sound models (without deadlocks and other anomalies), and guarantees to rediscover the underlying process in some cases. In this paper, we introduce a framework for process discovery that computes a directly-follows graph by passing over the log once, and applying a divide-and-conquer strategy. Moreover, we introduce three algorithms using the framework. We experimentally show that it sacrifices little compared to algorithms that use the full event log, while it gains the ability to cope with event logs of 100,000,000 traces and processes of 10,000 activities. Keywords: Big data; Scalable process mining; Block-structured process discovery; Directly-follows graphs; Rediscoverability
Original languageEnglish
Title of host publicationEnterprise, Business-Process and Information Systems Modeling (16th International Conference, BPMDS 2015, 20th International Conference, EMMSAD 2015, Held at CAiSE 2015, Stockholm, Sweden, June 8-9, 2015, Proceedings)
EditorsK. Gaaloul, R. Schmidt, S. Nurcan, S. Guerreiro, Q. Ma
Place of PublicationBerlin
PublisherSpringer
Pages85-101
ISBN (Print)978-3-319-19236-9
DOIs
Publication statusPublished - 2015

Publication series

NameLecture Notes in Business Information Processing
Volume214
ISSN (Print)1865-1348

Fingerprint

Dive into the research topics of 'Scalable process discovery with guarantees'. Together they form a unique fingerprint.

Cite this