Abstract
A significant task in business process optimization is concerned with streamlining the allocation and sharing of resources. This paper presents an approach for analyzing business process provisioning under a resource prediction strategy based on machine learning. A timed and probabilistic rewrite theory specification formalizes the semantics of business processes. It is integrated with an external oracle in the form of a long short-term memory neural network that can be queried to predict how traces of the process may advance within a time frame. Comparison of execution time and resource occupancy under different parameters is included for a case study, as well as details on the building of the machine learning model and its integration with Maude.
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Acknowledgments
The authors would like to thank the reviewers for carefully reading the manuscript; their comments have been of great help in improving its quality and clarity. The first author has been partially supported by projects PGC2018-094905-B-100 and UMA18-FEDERJA-180, and by Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech. The third author was partially supported by the ECOS-NORD MinCiencias C19M03 project “FACTS: Foundational Approach to Computation in Today’s Society”.
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Durán, F., Martínez, D., Rocha, C. (2022). Business Processes Analysis with Resource-Aware Machine Learning Scheduling in Rewriting Logic. In: Bae, K. (eds) Rewriting Logic and Its Applications. WRLA 2022. Lecture Notes in Computer Science, vol 13252. Springer, Cham. https://doi.org/10.1007/978-3-031-12441-9_6
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DOI: https://doi.org/10.1007/978-3-031-12441-9_6
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