Title:
How to evaluate process discovery for digital twins in industry 4.0? process discovery, hypothesis testing and conformance analysis
Authors:
- Juliano Yoshiro Nishiura
- Paulo Victor Lopes
- Filipe Alves Neto Verri
- Anders Skoogh
Published in:
(2024). ECMS 2024, 38th Proceedings
Edited by: Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev, European Council for Modelling and Simulation.
DOI: http://doi.org/10.7148/2024
ISSN: 2522-2422 (ONLINE)
ISSN: 2522-2414 (PRINT)
ISSN: 2522-2430 (CD-ROM)
ISBN: 978-3-937436-84-5
ISBN: 978-3-937436-83-8 (CD) Communications of the ECMS Volume 38, Issue 1, June 2024, Cracow, Poland June 4th – June 7th, 2024
DOI:
https://doi.org/10.7148/2024-0178
Citation format:
Juliano yoshiro nishiura, Paulo victor lopes, Filipe alves neto verri, Anders skoogh (2024). How to evaluate process discovery for Digital Twins in Industry 4.0? Process Discovery, Hypothesis Testing and Conformance Analysis, ECMS 2024, Proceedings Edited by: Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev, European Council for Modelling and Simulation. doi:10.7148/2024-0178
Abstract:
The field of data science is an emerging area of study that arises in the context of the production of a large volume of data in recent years. The objective of this area is to obtain valuable information that is extracted through data processing. In the industrial context, the identification of failures and bottlenecks in production lines is essential to increase the productivity of the evaluated systems. However, manual analysis can be time-consuming and costly. Process discovery is a set of techniques that includes the use of algorithms to extract a process model from the event log, which can be used as a basis for developing Digital Twins. Therefore, this paper proposes the use of an artificial production line generator so that process mining algorithms can be tested with a large number of samples and different network characteristics. Thus, the main contribution will be the testing of hypotheses to assist in choosing the best algorithms in a practical context.
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