Relational XES: Data management for process mining

B.F. Dongen, van, S. Shabani

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

17 Citations (Scopus)
7 Downloads (Pure)


Information systems log data during the execution of business processes in so called "event logs". Process mining aims to improve business processes by extracting knowledge from event logs. Currently, the de-facto standard for storing and managing event data, XES, is tailored towards sequential access of this data. Handling more and more data in process mining applications is an important challenge and there is a need for standardized ways of storing and processing event data in the large. In this paper, we first discuss several solutions to address the "big data" problem in process mining. We present a new framework for dealing with large event logs using a relational data model which is backwards compatible with XES. This framework, called Relational XES, provides buffered, random access to events resulting in a reduction of memory usage and we present experiments with existing process mining applications to show how this framework trades memory for CPU time.
Original languageEnglish
Title of host publicationProceedings of the CAiSE 2015 Forum at the 27th International Conference on Advanced Information Systems Engineering, Stockholm, Sweden, June 10, 2015
EditorsJ. Grabis, K. Sandkuhl
Place of PublicationAachen
Publication statusPublished - 2015
Event27th International Conference on Advanced Information Systems Engineering (CAiSE 2015) - Stockholm, Sweden
Duration: 8 Jun 201512 Jun 2015

Publication series

NameCEUR Workshop Proceedings
ISSN (Print)1613-0073


Conference27th International Conference on Advanced Information Systems Engineering (CAiSE 2015)
Abbreviated titleCAiSE 2015
Internet address


Dive into the research topics of 'Relational XES: Data management for process mining'. Together they form a unique fingerprint.

Cite this