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Authors: 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 and Hélio Lopes

Affiliation: Pontifícia Universidade Católica do Rio de Janeiro, Brazil

Keyword(s): Visual Filtering, Process Mining, Event Log, Multidimensional Projection.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Human-Computer Interaction ; Interaction Techniques and Devices ; Sensor Networks ; Signal Processing ; Soft Computing

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.

CC BY-NC-ND 4.0

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Paper citation in several formats:
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; ISSN 2184-4992, SciTePress, pages 38-49. DOI: 10.5220/0006708200380049

@conference{iceis18,
author={Luiz Schirmer. and Leonardo {Quatrin Campagnolo}. and Sonia {Fiol González}. and Ariane {M. B. Rodrigues}. and Guilherme {G. Schardong}. and Rafael Fran\c{C}a. 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},
issn={2184-4992},
}

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
IS - 2184-4992
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
PB - SciTePress