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HOW TO EVALUATE PRO...
HOW TO EVALUATE PROCESS DISCOVERY FOR DIGITAL TWINS IN INDUSTRY 4.0? PROCESS DISCOVERY, HYPOTHESIS TESTING, AND CONFORMANCE ANALYSIS
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- Yoshiro, Juliano (author)
- Instituto Tecnológico de Aeronáutica (ITA)
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- Lopes, Paulo Victor, 1996 (author)
- Instituto Tecnológico de Aeronáutica (ITA),Chalmers tekniska högskola,Chalmers University of Technology
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- Neto Verri, Filipe Alves (author)
- Instituto Tecnológico de Aeronáutica (ITA)
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- Skoogh, Anders, 1980 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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(creator_code:org_t)
- 2024
- 2024
- English.
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In: Proceedings - European Council for Modelling and Simulation, ECMS. - 2522-2414. ; 38:1, s. 178-184
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Production Lines
- Conformance Analysis
- Bootstrap Simulation
- Hypothesis Testing
Publication and Content Type
- kon (subject category)
- ref (subject category)
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