Abstract
The automation of sequences of repetitive actions performed by human operators in interacting with software applications is crucial to prevent work from being perceived as alienating and boring. Robot applications can automatise these sequences once they have been identified. In this paper, we propose a two-step approach to mine sequences of actions that could be automated from log data produced by the interactions of a human operator with specific software applications. Since the number of possible sequences may be very high and not all the sequences are interesting to be automatised, we focus our mining process on sequences that meet precise patterns. First, Frequent Episode Mining algorithms are applied for extracting all the sequences of actions that occur with at least a minimum frequency. Then, we exploit fuzzy string matching based on the Levenshtein distance for filtering out the sequences that do not match established patterns. We evaluate the effectiveness of the approach using a benchmark dataset and present a case study on a real-world dataset of activity logs generated in the context of the AUTOMIA project.
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Dell’Oglio, P., Bondielli, A., Bechini, A., Marcelloni, F. (2023). Leveraging Sequence Mining for Robot Process Automation. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 717. Springer, Cham. https://doi.org/10.1007/978-3-031-35510-3_22
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