Dataset for Laakmann et al. "A Nascent Taxonomy of Machine Learning in Intelligent Robotic Process Automation"


Dataset for the article: Laakmann, Lukas ; Ciftci, Seyyid A. ; Janiesch, Christian : A Nascent Taxonomy of Machine Learning in Intelligent Robotic Process Automation. Accepted at: Proceedings of the 22nd International Conference on Business Process Management BPM Forum 2024.

Abstract: Robotic process automation (RPA) is a lightweight approach to automating business processes using software robots that emulate user actions at the graphical user interface level. While RPA has gained popularity for its cost-effective and timely automation of rule-based, well-structured tasks, its symbolic nature has inherent limitations when approaching more complex tasks currently performed by human agents. Machine learning concepts enabling intelligent RPA provide an opportunity to broaden the range of automatable tasks. In this paper, we conduct a literature review to explore the connections between RPA and machine learning and organize the joint concept intelligent RPA into a taxonomy. Our taxonomy comprises the two meta-characteristics RPA-ML integration and RPA-ML interaction. Together, they comprise eight dimensions: architecture and ecosystem, capabilities, data basis, intelligence level, and technical depth of integration as well as deployment environment, lifecycle phase, and user-robot relation.

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Metadata Access
Creator Laakmann, Lukas; Ciftci, Seyyid A.; Janiesch, Christian
Publisher EUDAT B2SHARE; TU Dortmund University
Publication Year 2024
Rights Creative Commons Attribution-NonCommercial-ShareAlike (CC-BY-NC-SA); info:eu-repo/semantics/openAccess
OpenAccess true
Contact christian.janiesch(at)
Language English
Resource Type Dataset
Format pdf
Size 171.9 kB; 1 file
Version 1
Discipline → Information systems → Management information systems