A MapReduce Approach for Mining Multi-Perspective Declarative Process Models

Christian Sturm, Stefan Schönig, Stefan Jablonski

2018

Abstract

Automated process discovery aims at generating a process model from an event log. Such models can be represented as a set of declarative constraints where temporal coherencies can also be intertwined with dependencies upon value ranges of data parameters and resource characteristics. Existing mining tools do not support multi-perspective constraint discovery or are not efficient enough. In this paper, we propose an efficient mining framework for discovering multi-perspective declarative models that builds upon the distributed processing method MapReduce. Mining performance and effectiveness have been tested on several real-life event logs.

Paper Citation

in Harvard Style

Sturm C., Schönig S. and Jablonski S. (2018). A MapReduce Approach for Mining Multi-Perspective Declarative Process Models.In Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-298-1, pages 585-595. DOI: 10.5220/0006710305850595

in Bibtex Style

@conference{iceis18,
author={Christian Sturm and Stefan Schönig and Stefan Jablonski},
title={A MapReduce Approach for Mining Multi-Perspective Declarative Process Models},
booktitle={Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2018},
pages={585-595},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006710305850595},
isbn={978-989-758-298-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - A MapReduce Approach for Mining Multi-Perspective Declarative Process Models
SN - 978-989-758-298-1
AU - Sturm C.
AU - Schönig S.
AU - Jablonski S.
PY - 2018
SP - 585
EP - 595
DO - 10.5220/0006710305850595