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Democratizing Robotic Process Mining: A Conceptual Framework for User Actions, Tasks, and RPA Bots

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Business Process Management: Blockchain, Robotic Process Automation, Central and Eastern European, Educators and Industry Forum (BPM 2024)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 527))

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Abstract

While Robotic Process Automation (RPA) enables companies to automate tasks in a comparable lightweight manner, the creation of such automations still requires a considerable manual effort. Robotic Process Mining (RPM) tries to mitigate this effort by analyzing recorded user interactions with a computer system to identify routines automatable by RPA and to derive corresponding RPA scripts. The rapid evolution of both RPA and RPM has led to ambiguities in the terminology used across research, prototypes, and industry. In this paper, we survey and structure the possible types of user interactions that can be recorded by analyzing the literature on RPM and the related field of user behavior mining. Additionally, we aim to foster a unified understanding of the key terms and concepts in RPM by proposing a conceptualization that draws upon ontologies of software, user interfaces, and RPA to embed this comparably new research area. To explore the practical applicability of the conceptualization, we conclude with a number of concrete usage scenarios that strengthen the design and development of RPA bots.

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Notes

  1. 1.

    Complete list and mapping: https://zenodo.org/doi/10.5281/zenodo.12635361.

  2. 2.

    The concept User is not part of this conceptualization. However, \(\textrm{UI}_{2}\textrm{Ont}\), CSO, and DnS already include the notion of a user so that this aspect can be incorporated in future work.

  3. 3.

    The granularity of log entries created by different tools can vary as they already may perform some post-processing, like combining focussing the UI element and entering text.

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Author information

Authors and Affiliations

  1. Katholische Universität Eichstätt-Ingolstadt, Eichstätt, Germany

    Tom Hohenadl

  2. Technische Hochschule Ingolstadt, Ingolstadt, Germany

    Tom Hohenadl & Christian Stummeyer

  3. Hasso Plattner Institute, University of Potsdam, Potsdam, Germany

    Maximilian Völker & Mathias Weske

Authors
  1. Tom Hohenadl
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  2. Maximilian Völker
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  3. Christian Stummeyer
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  4. Mathias Weske
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Correspondence to Tom Hohenadl .

Editor information

Editors and Affiliations

  1. Utrecht University, Utrecht, The Netherlands

    Claudio Di Ciccio

  2. University of Vienna, Vienna, Austria

    Walid Fdhila

  3. Sapienza University of Rome, Rome, Italy

    Simone Agostinelli

  4. University of Ottawa, Ottawa, ON, Canada

    Daniel Amyot

  5. Kühne Logistics University, Hamburg, Germany

    Henrik Leopold

  6. Masaryk University, Brno, Czech Republic

    Michal Krčál

  7. TU Wien, Vienna, Austria

    Monika Malinova Mandelburger

  8. University of Maribor, Maribor, Slovenia

    Gregor Polančič

  9. University of Zagreb, Varaždin, Croatia

    Katarina Tomičić-Pupek

  10. AGH University of Krakow, Kraków, Poland

    Katarzyna Gdowska

  11. University of St. Gallen, St. Gallen, Switzerland

    Thomas Grisold

  12. University of Gdańsk, Gdańsk, Poland

    Piotr Sliż

  13. Utrecht University, Utrecht, The Netherlands

    Iris Beerepoot

  14. University of Warsaw, Warsaw, Poland

    Renata Gabryelczyk

  15. University of Duisburg-Essen, Essen, Germany

    Ralf Plattfaut

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Hohenadl, T., Völker, M., Stummeyer, C., Weske, M. (2024). Democratizing Robotic Process Mining: A Conceptual Framework for User Actions, Tasks, and RPA Bots. In: Di Ciccio, C., et al. Business Process Management: Blockchain, Robotic Process Automation, Central and Eastern European, Educators and Industry Forum. BPM 2024. Lecture Notes in Business Information Processing, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-031-70445-1_12

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