Visual Support to Filtering Cases for Process Discovery

Luiz Schirmer, Leonardo Quatrin Campagnolo, Sonia Fiol González, Ariane M. B. Rodrigues, Guilherme G. Schardong, Rafael França, Mauricio Lana, Simone D. J. Barbosa, Marcus Poggi, Hélio Lopes

Abstract

Working with average-sized event logs is still a major task in process mining, where the main goal is to gain process-related insights based on event logs created by a wide variety of systems. An event log contains a sequence of events for every case that was handled by the system. Several discovery algorithms have been proposed and work well in specific cases but fail to be generic strategies. Moreover, there is no evidence that the existing strategies can handle events with a large number of variants. For this reason, a generic approach is needed to allow experts to explore event log data and decompose information into a series of smaller problems, to identify outliers and relations between the analyzed cases. In this paper we present a visual filtering approach for event logs that makes process analysis tasks more feasible and tractable. To evaluate our approach, we have developed a visual filtering tool and used it with the event log from BPI Challenge 2017.

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Paper Citation


in Harvard Style

Schirmer L., Quatrin Campagnolo L., Fiol González S., M. B. Rodrigues A., G. Schardong G., França R., Lana M., D. J. Barbosa S., Poggi M. and Lopes H. (2018). Visual Support to Filtering Cases for Process Discovery.In Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-298-1, pages 38-49. DOI: 10.5220/0006708200380049


in Bibtex Style

@conference{iceis18,
author={Luiz Schirmer and Leonardo Quatrin Campagnolo and Sonia Fiol González and Ariane M. B. Rodrigues and Guilherme G. Schardong and Rafael França and Mauricio Lana and Simone D. J. Barbosa and Marcus Poggi and Hélio Lopes},
title={Visual Support to Filtering Cases for Process Discovery},
booktitle={Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2018},
pages={38-49},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006708200380049},
isbn={978-989-758-298-1},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Visual Support to Filtering Cases for Process Discovery
SN - 978-989-758-298-1
AU - Schirmer L.
AU - Quatrin Campagnolo L.
AU - Fiol González S.
AU - M. B. Rodrigues A.
AU - G. Schardong G.
AU - França R.
AU - Lana M.
AU - D. J. Barbosa S.
AU - Poggi M.
AU - Lopes H.
PY - 2018
SP - 38
EP - 49
DO - 10.5220/0006708200380049