Use of FURIA for Improving Task Mining

Abstract

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.