Robotic Process Automation: Hype or Hope? - Hindel, Julia; Cabrera Pérez, Lena; Stierle, Matthias (Proceedings of the 15th International Conference on Wirtschaftsinformatik, 2020)
Robotic Process Automation (RPA) is a fast-emerging process automation technology suited for high-volume, repetitive, and rule-based tasks. The promises of rising RPA vendors and the lack of documented track records leave researchers and practitioners with the challenge of positioning the term and assessing RPA’s true potential. To objectively discuss the strengths and weaknesses of this technology, we conduct a literature review, a practical implementation of an RPA solution, and an interview with an industry expert. We reveal that the current literature primarily focuses on economic factors. This paper, therefore, adds various social and technical aspects to the discussion. Most importantly, robustness and stability pose technical challenges for successfully implementing RPA. Further research directed at error handling and maintenance of software robots is required to support the successful implementation of RPA.<br
Keywords: Robotic Process Automation; Software Robotics; Business Process Management; Automation

Text-aware predictive process monitoring with contextualized word embeddings - Cabrera Pérez, Lena; Weinzierl, Sven; Zilker, Sandra; Matzner, Martin (Proceedings of the BPM 2022 International Workshops, 2022)
Predictive process monitoring (PPM) is the discipline of exploiting event logs of business processes to construct predictive models for anticipating different properties of running business processes. The event logs used contain control flow information of past process executions and, often, additional information about the context in which a process ran. As the process context can add valuable information to a predictive model, recent PPM techniques often incorporate it to improve process predictions. While most techniques incorporate context information as well-structured numerical and categorical context features, only a few utilize unstructured text from process-related comment fields, emails, or documents. The few existing text-aware PPM approaches are limited in capturing semantic information, as different meanings of the same word occurring in different contexts, i.e., sentences, are ignored. This paper addresses this limitation by proposing a text-aware PPM technique using contextualized word embeddings to predict the next activity and the next timestamp of running process instances. An experimental evaluation with a text-enriched real-life event log shows that our technique can outperform text-aware PPM approaches relying on non-contextualized word embeddings in terms of predictive performanc
Keywords: business process management; predictive process monitoring; machine learning; deep learning; natural language processing