loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Takeharu Mitsuda 1 ; Hiroyuki Nakagawa 1 ; 2 ; Haruhiko Kaiya 3 ; Hironori Takeuchi 4 ; Sinpei Ogata 5 and Tatsuhiro Tsuchiya 1

Affiliations: 1 Osaka University, Japan ; 2 Okayama University, Japan ; 3 Kanagawa University, Japan ; 4 Musashi University, Japan ; 5 Shinshu University, Japan

Keyword(s): Process Mining, Process Discovery, HeuristicsMiner.

Abstract: HeuristicsMiner is a process mining technique, which can construct a process model representing dependency relations of each activity from event logs. HeuristicsMiner is notable for its ability to output a process model that removes noise from the input data by allowing the user to set multiple parameters. However, it is difficult for users to understand the characteristics of each parameter and to identify parameter values that enable them to obtain ideal process models. In this study, we propose a method for identifying all possible process models that can be generated from an input event log in HeuristicsMiner. We extract the conditions under which the dependencies in the input logs are represented in the output model, and then create a process model transition table based on these conditions to identify these models. We applied this method to several large logs and mined process models using the combinations of parameter values obtained, and confirmed that process models were eff iciently obtained without excesses or deficiencies. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 52.15.225.105

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Mitsuda, T., Nakagawa, H., Kaiya, H., Takeuchi, H., Ogata, S. and Tsuchiya, T. (2025). Exhaustive Model Identification on Process Mining. In Proceedings of the 20th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE; ISBN 978-989-758-742-9; ISSN 2184-4895, SciTePress, pages 449-456. DOI: 10.5220/0013270400003928

@conference{enase25,
author={Takeharu Mitsuda and Hiroyuki Nakagawa and Haruhiko Kaiya and Hironori Takeuchi and Sinpei Ogata and Tatsuhiro Tsuchiya},
title={Exhaustive Model Identification on Process Mining},
booktitle={Proceedings of the 20th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE},
year={2025},
pages={449-456},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013270400003928},
isbn={978-989-758-742-9},
issn={2184-4895},
}

TY - CONF

JO - Proceedings of the 20th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE
TI - Exhaustive Model Identification on Process Mining
SN - 978-989-758-742-9
IS - 2184-4895
AU - Mitsuda, T.
AU - Nakagawa, H.
AU - Kaiya, H.
AU - Takeuchi, H.
AU - Ogata, S.
AU - Tsuchiya, T.
PY - 2025
SP - 449
EP - 456
DO - 10.5220/0013270400003928
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic