Acta Informatica Pragensia 2022, 11(2), 241-253 | DOI: 10.18267/j.aip.1832141

Use of FURIA for Improving Task Mining

Petr Prùcha ORCID..., Jan Skrbek ORCID...
Faculty of Economics, Technical University of Liberec, Studentská 1402/2, 461 17 Liberec, Czech Republic

Companies that use robotic process automation very often deal with the problem of selecting a suitable process for automation. Manual selection of a suitable process is very time-consuming. Therefore, part of the process mining field specializes in selecting suitable processes for automation based on process data. This work deals with the possibility of improving the existing method for finding suitable candidates for automation. To improve the current approach, we remove the limiting restrictions of the current method and use another FURIA rule-learning algorithm for rule detection. We use three different datasets and the WEKA platform to validate the results. The results show that FURIA and the removal of strictly deterministic rules as restrictions turned out to be a competitive approach to the original one. On data presented in this study, the selected approach detected more candidates for automation and with higher accuracy. This study implies that FURIA and not using a strictly deterministic process is an appropriate procedure with certain use cases as other procedures mentioned in this study.

Keywords: FURIA; Task mining; RPA; Robotic process automation; RIPPER; Automatable routines.

Received: January 13, 2022; Revised: June 18, 2022; Accepted: June 21, 2022; Prepublished online: July 2, 2022; Published: August 19, 2022  Show citation

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Prùcha, P., & Skrbek, J. (2022). Use of FURIA for Improving Task Mining. Acta Informatica Pragensia, 11(2), 241-253. doi: 10.18267/j.aip.183
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