Bot Log Mining : An Approach to the Integrated Analysis of Robotic Process Automation and Process Mining

Titelangaben

Egger, Andreas ; ter Hofstede, Arthur H. M. ; Kratsch, Wolfgang ; Leemans, Sander J. J. ; Röglinger, Maximilian ; Wynn, Moe T.:
Bot Log Mining : An Approach to the Integrated Analysis of Robotic Process Automation and Process Mining.
In: Information Systems. Bd. 126 (2024) . - 102431.
ISSN 0306-4379
DOI: https://doi.org/10.1016/j.is.2024.102431

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Abstract

Process mining and Robotic Process Automation (RPA) are two technologies of great interest in research and practice. Process mining uses event logs as input, but much of the information available about processes is not yet considered since the data is outside the scope of ordinary event logs. RPA technology can automate tasks by using bots, and the executed steps can be recorded, which could be a valuable data source for process mining. With the use of RPA technology expected to grow, an integrated view of steps performed by bots in business processes is needed. In process mining, various techniques to analyze processes have already been developed. Most RPA software also includes basic measures to monitor bot performance. However, the isolated use of bot-related or process mining measures does not provide an end-to-end view of bot-enabled business processes. To address these issues, we develop an approach that enables using RPA logs for process mining and propose tailored measures to analyze merged bot and process logs. We use the design science research process to structure our work and evaluate the approach by conducting a total of 14 interviews with experts from industry and research. We also implement a software prototype and test it on real-world and artificial data. This approach contributes to prescriptive knowledge by providing a concept on how to use bot logs for process mining and brings the research streams of RPA and process mining further together. It provides new data that expands the possibilities of existing process mining techniques in research and practice, and it enables new analyses that can observe bot-human interaction and show the effects of bots on business processes.