Identifying Candidate Tasks for Robotic Process Automation in Textual Process Descriptions

  • Henrik Leopold
  • Han van der AaEmail author
  • Hajo A. Reijers
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 318)


The continuous digitization requires organizations to improve the automation of their business processes. Among others, this has lead to an increased interest in Robotic Process Automation (RPA). RPA solutions emerge in the form of software that automatically executes repetitive and routine tasks. While the benefits of RPA on cost savings and other relevant performance indicators have been demonstrated in different contexts, one of the key challenges for RPA endeavors is to effectively identify processes and tasks that are suitable for automation. Textual process descriptions, such as work instructions, provide rich and important insights about this matter. However, organizations often maintain hundreds or even thousands of them, which makes a manual analysis unfeasible for larger organizations. Recognizing the large manual effort required to determine the current degree of automation in an organization’s business processes, we use this paper to propose an approach that is able to automatically do so. More specifically, we leverage supervised machine learning to automatically identify whether a task described in a textual process description is manual, an interaction of a human with an information system or automated. An evaluation with a set of 424 activities from a total of 47 textual process descriptions demonstrates that our approach produces satisfactory results.


Textual Process Descriptions Robotic Process Automation (RPA) Linguistic Preprocessing Steps Vacation Request Recording Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Van der Aa, H., Leopold, H., Reijers, H.A.: Dealing with behavioral ambiguity in textual process descriptions. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 271–288. Springer, Cham (2016). Scholar
  2. 2.
    Van der Aa, H., Leopold, H., Reijers, H.A.: Comparing textual descriptions to process models: the automatic detection of inconsistencies. Inf. Syst. 64, 447–460 (2017)CrossRefGoogle Scholar
  3. 3.
    Van der Aa, H., Leopold, H., van de Weerd, I., Reijers, H.A.: Causes and consequences of fragmented process information: insights from a case study. In: Proceedings of the Annual Americas’ Conference on Information Systems (2017)Google Scholar
  4. 4.
    van der Aalst, W.M.P., Barros, A.P., ter Hofstede, A.H.M., Kiepuszewski, B.: Advanced workflow patterns. In: Scheuermann, P., Etzion, O. (eds.) CoopIS 2000. LNCS, vol. 1901, pp. 18–29. Springer, Heidelberg (2000). Scholar
  5. 5.
    Aguirre, S., Rodriguez, A.: Automation of a business process using robotic process automation (RPA): a case study. In: Figueroa-García, J.C., López-Santana, E.R., Villa-Ramírez, J.L., Ferro-Escobar, R. (eds.) WEA 2017. CCIS, vol. 742, pp. 65–71. Springer, Cham (2017). Scholar
  6. 6.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)Google Scholar
  7. 7.
    Davenport, T.H., Kirby, J.: Just how smart are smart machines? MIT Sloan Manage. Rev. 57(3), 21 (2016)Google Scholar
  8. 8.
    Domingos, P.: A few useful things to know about machine learning. Commun. ACM 55(10), 78–87 (2012)CrossRefGoogle Scholar
  9. 9.
    Dumas, M., Rosa, M., Mendling, J., Reijers, H.: Fundamentals of Business Process Management. Springer, Heidelberg (2013). Scholar
  10. 10.
    Friedrich, F., Mendling, J., Puhlmann, F.: Process model generation from natural language text. In: Mouratidis, H., Rolland, C. (eds.) CAiSE 2011. LNCS, vol. 6741, pp. 482–496. Springer, Heidelberg (2011). Scholar
  11. 11.
    Fung, H.P.: Criteria, use cases and effects of information technology process automation (ITPA). Browser Download This Paper (2014)Google Scholar
  12. 12.
    Gacitua-Decar, V., Pahl, C.: Automatic business process pattern matching for enterprise services design. In: IEEE Congress on Services Part II, pp. 111–118 (2009)Google Scholar
  13. 13.
    Georgakopoulos, D., Hornick, M., Sheth, A.: An overview of workflow management: from process modeling to workflow automation infrastructure. Distrib. Parallel Databases 3(2), 119–153 (1995)CrossRefGoogle Scholar
  14. 14.
    Ghose, A.K., Koliadis, G., Chueng, A.: Process discovery from model and text artefacts. In: Proceedings of the IEEE Congress on Services, pp. 167–174. IEEE Computer Society (2007)Google Scholar
  15. 15.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newslett. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  16. 16.
    Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998). Scholar
  17. 17.
    Kirchmer, M.: Robotic process automation-pragmatic solution or dangerous illusion? BTOES Insights, June 2017Google Scholar
  18. 18.
    Klein, D., Manning, C.D.: Accurate unlexicalized parsing. In: 41st Meeting of the Association for Computational Linguistics, pp. 423–430 (2003)Google Scholar
  19. 19.
    Klinkmüller, C., Weber, I., Mendling, J., Leopold, H., Ludwig, A.: Increasing recall of process model matching by improved activity label matching. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 211–218. Springer, Heidelberg (2013). Scholar
  20. 20.
    Lacity, M., Willcocks, L.P., Craig, A.: Robotic process automation at telefonica O2 (2015)Google Scholar
  21. 21.
    Lacity, M.C., Willcocks, L.P.: A new approach to automating services. MIT Sloan Manage. Rev. 58(1), 41 (2016)Google Scholar
  22. 22.
    Leopold, H., van der Aa, H., Pittke, F., Raffel, M., Mendling, J., Reijers, H.A.: Searching textual and model-based process descriptions based on a unified data format. Softw. Syst. Model. 1–16 (2017)Google Scholar
  23. 23.
    Leopold, H., Eid-Sabbagh, R.H., Mendling, J., Azevedo, L.G., Baião, F.A.: Detection of naming convention violations in process models for different languages. Decis. Support Syst. 56, 310–325 (2013)CrossRefGoogle Scholar
  24. 24.
    Leopold, H., Mendling, J.: Automatic derivation of service candidates from business process model repositories. In: Abramowicz, W., Kriksciuniene, D., Sakalauskas, V. (eds.) BIS 2012. LNBIP, vol. 117, pp. 84–95. Springer, Heidelberg (2012). Scholar
  25. 25.
    Leopold, H., Niepert, M., Weidlich, M., Mendling, J., Dijkman, R., Stuckenschmidt, H.: Probabilistic optimization of semantic process model matching. In: Barros, A., Gal, A., Kindler, E. (eds.) BPM 2012. LNCS, vol. 7481, pp. 319–334. Springer, Heidelberg (2012). Scholar
  26. 26.
    Leopold, H., Smirnov, S., Mendling, J.: On the refactoring of activity labels in business process models. Inf. Syst. 37(5), 443–459 (2012)CrossRefGoogle Scholar
  27. 27.
    Levin, B.: English Verb Classes and Alternations: A Preliminary Investigation. University of Chicago Press, Chicago (1993)Google Scholar
  28. 28.
    Leyh, C., Bley, K., Seek, S.: Elicitation of processes in business process management in the era of digitization – the same techniques as decades ago? In: Piazolo, F., Geist, V., Brehm, L., Schmidt, R. (eds.) ERP Future 2016. LNBIP, vol. 285, pp. 42–56. Springer, Cham (2017). Scholar
  29. 29.
    Miller, G., Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)Google Scholar
  30. 30.
    Pittke, F., Leopold, H., Mendling, J.: Automatic detection and resolution of lexical ambiguity in process models (2015)Google Scholar
  31. 31.
    Riefer, M., Ternis, S.F., Thaler, T.: Mining process models from natural language text: A state-of-the-art analysis. In: Multikonferenz Wirtschaftsinformatik (MKWI-16). Universität Illmenau , Illmenau, Germany, 9–11 March 2016Google Scholar
  32. 32.
    Sànchez-Ferreres, J., Carmona, J., Padró, L.: Aligning textual and graphical descriptions of processes through ILP techniques. In: Dubois, E., Pohl, K. (eds.) CAiSE 2017. LNCS, vol. 10253, pp. 413–427. Springer, Cham (2017). Scholar
  33. 33.
    Schuler, K.K.: Verbnet: a broad-coverage, comprehensive verb lexicon. Ph.D. thesis, Philadelphia, PA, USA (2005)Google Scholar
  34. 34.
    Stohr, E.A., Zhao, J.L.: Workflow automation: overview and research issues. Inf. Syst. Front. 3(3), 281–296 (2001)CrossRefGoogle Scholar
  35. 35.
    Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. J. Mach. Learn. Res. 2, 45–66 (2001)Google Scholar
  36. 36.
    Walenz, B., Didion, J.: JWNL: Java wordnet library (2011)Google Scholar
  37. 37.
    Willcocks, L., Lacity, M.C.: Service Automation: Robots and the Future of Work. Steve Brookes Publishing, Warwickshire (2016)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Henrik Leopold
    • 1
  • Han van der Aa
    • 1
    Email author
  • Hajo A. Reijers
    • 1
  1. 1.Vrije Universiteit AmsterdamAmsterdamThe Netherlands

Personalised recommendations