Skip to main content

Mastering Robotic Process Automation with Process Mining

  • Conference paper
  • First Online:
Business Process Management (BPM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13420))

Included in the following conference series:

Abstract

Robotic Process Automation (RPA) is an emerging automation technology that creates software (SW) robots to partially or fully automate rule-based and repetitive tasks (aka routines) previously performed by human users in their applications’ user interfaces (UIs). Successful usage of RPA requires strong support by skilled human experts, from the detection of the routines to be automated to the development of the executable scripts required to enact SW robots. In this paper, we discuss how process mining can be leveraged to minimize the manual and time-consuming steps required for the creation of SW robots, enabling new levels of automation and support for RPA. We first present a reference data model that can be used for a standardized specification of UI logs recording the interactions between workers and SW applications to enable interoperability among different tools. Then, we introduce a pipeline of processing steps that enable us to (1) semi-automatically discover the anatomy of a routine directly from the UI logs, and (2) automatically develop executable scripts for performing SW robots at run-time. We show how this pipeline can be effectively enacted by researchers/practitioners through the SmartRPA tool.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://github.com/bpm-diag/smartRPA.

References

  1. van der Aalst, W.M.P., Bichler, M., Heinzl, A.: Robotic process automation. Bus. Inf. Syst. Eng. 60(4), 269–272 (2018). https://doi.org/10.1007/s12599-018-0542-4

    Article  Google Scholar 

  2. Abb, L., Rehse, J.R.: A reference data model for process-related user interaction logs. In: Di Ciccio, C., et al. (eds.) BPM 2022, LNCS 13420, pp. 57–74. Springer, Cham (2022)

    Google Scholar 

  3. Agostinelli, S., Leotta, F., Marrella, A.: Interactive segmentation of user interface logs. In: Hacid, H., Kao, O., Mecella, M., Moha, N., Paik, H. (eds.) ICSOC 2021. LNCS, vol. 13121, pp. 65–80. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-91431-8_5

    Chapter  Google Scholar 

  4. Agostinelli, S., Lupia, M., Marrella, A., Mecella, M.: Automated generation of executable RPA scripts from user interface logs. In: Asatiani, A., et al. (eds.) BPM 2020. LNBIP, vol. 393, pp. 116–131. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58779-6_8

    Chapter  Google Scholar 

  5. Agostinelli, S., Lupia, M., Marrella, A., Mecella, M.: SmartRPA: a tool to reactively synthesize software robots from user interface logs. In: Nurcan, S., Korthaus, A. (eds.) CAiSE 2021. LNBIP, vol. 424, pp. 137–145. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79108-7_16

    Chapter  Google Scholar 

  6. Agostinelli, S., Lupia, M., Marrella, A., Mecella, M.: Reactive Synthesis of Software Robots in RPA from User Interface Logs. Computers in Industry (2022)

    Google Scholar 

  7. Agostinelli, S., Marrella, A., Mecella, M.: Research challenges for intelligent robotic process automation. In: Di Francescomarino, C., Dijkman, R., Zdun, U. (eds.) BPM 2019. LNBIP, vol. 362, pp. 12–18. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-37453-2_2

    Chapter  Google Scholar 

  8. Agostinelli, S., Marrella, A., Mecella, M.: Towards Intelligent Robotic Process Automation for BPMers (2020). https://arxiv.org/abs/2001.00804

  9. Agostinelli, S., Marrella, A., Mecella, M.: Exploring the challenge of automated segmentation in robotic process automation. In: Cherfi, S., Perini, A., Nurcan, S. (eds.) RCIS 2021. LNBIP, vol. 415, pp. 38–54. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75018-3_3

    Chapter  Google Scholar 

  10. Chakraborti, T., et al.: From robotic process automation to intelligent process automation. In: Asatiani, A., et al. (eds.) BPM 2020. LNBIP, vol. 393, pp. 215–228. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58779-6_15

    Chapter  Google Scholar 

  11. Cook, D.J., Krishnan, N.C., Rashidi, P.: Activity discovery and activity recognition: a new partnership. IEEE Trans. Cybern. 43(3), 820–828 (2013). https://doi.org/10.1109/TSMCB.2012.2216873

    Article  Google Scholar 

  12. de Leoni, M., Lanciano, G., Marrella, A.: Aligning partially-ordered process-execution traces and models using automated planning. In: Twenty-Eight International Conference on Automated Planning and Scheduling (ICAPS 2018), pp. 321–329 (2018). https://aaai.org/ocs/index.php/ICAPS/ICAPS18/paper/view/17739

  13. van Der Aalst, W.M., Pesic, M., Schonenberg, H.: declarative workflows: balancing between flexibility and support. Comp. Sc.-Res. Dev. 23(2) (2009). https://doi.org/10.1007/s00450-009-0057-9

  14. Jimenez-Ramirez, A., Reijers, H.A., Barba, I., Del Valle, C.: A method to improve the early stages of the robotic process automation lifecycle. In: Giorgini, P., Weber, B. (eds.) CAiSE 2019. LNCS, vol. 11483, pp. 446–461. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21290-2_28

    Chapter  Google Scholar 

  15. Leno, V., Deviatykh, S., Polyvyanyy, A., Rosa, M.L., Dumas, M., Maggi, F.M.: Robidium: automated synthesis of robotic process automation scripts from UI logs. In: BPM Demonstration and Resources (2020). https://ceur-ws.org/Vol-2673/paperDR08.pdf

  16. Leno, V., Polyvyanyy, A., Dumas, M., La Rosa, M., Maggi, F.M.: Robotic process mining: vision and challenges. Bus. Inf. Syst. Eng. 63(3), 301–314 (2020). https://doi.org/10.1007/s12599-020-00641-4

    Article  Google Scholar 

  17. Leno, V., Polyvyanyy, A., Rosa, M.L., Dumas, M., Maggi, F.M.: Action logger: enabling process mining for robotic process automation. In: BPM Demonstration and Resources (2019). https://ceur-ws.org/Vol-2420/paperDT2.pdf

  18. Linn, C., Zimmermann, P., Werth, D.: Activity mining - a new level of detail in mining business processes. In: Workshops der INFORMATIK, pp. 245–258 (2018). https://dl.gi.de/20.500.12116/17225

Download references

Acknowledgments

This work has been partially supported by the H2020 project DataCloud and the Sapienza grant BPbots.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andrea Marrella .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Agostinelli, S., Marrella, A., Abb, L., Rehse, JR. (2022). Mastering Robotic Process Automation with Process Mining. In: Di Ciccio, C., Dijkman, R., del Río Ortega, A., Rinderle-Ma, S. (eds) Business Process Management. BPM 2022. Lecture Notes in Computer Science, vol 13420. Springer, Cham. https://doi.org/10.1007/978-3-031-16103-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-16103-2_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16102-5

  • Online ISBN: 978-3-031-16103-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics