Abstract
The robotic automation of processes is of much interest to organizations. A common use case is to automate the repetitive manual tasks (or processes) that are currently done by back-office staff through some information system (IS). The lifecycle of any Robotic Process Automation (RPA) project starts with the analysis of the process to automate. This is a very time-consuming phase, which in practical settings often relies on the study of process documentation. Such documentation is typically incomplete or inaccurate, e.g., some documented cases never occur, occurring cases are not documented, or documented cases differ from reality. To deploy robots in a production environment that are designed on such a shaky basis entails a high risk. This paper describes and evaluates a new proposal for the early stages of an RPA project: the analysis of a process and its subsequent design. The idea is to leverage the knowledge of back-office staff, which starts by monitoring them in a non-invasive manner. This is done through a screen-mouse-key-logger, i.e., a sequence of images, mouse actions, and key actions are stored along with their timestamps. The log which is obtained in this way is transformed into a UI log through image-analysis techniques (e.g., fingerprinting or OCR) and then transformed into a process model by the use of process discovery algorithms. We evaluated this method for two real-life, industrial cases. The evaluation shows clear and substantial benefits in terms of accuracy and speed. This paper presents the method, along with a number of limitations that need to be addressed such that it can be applied in wider contexts.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
These event attributes are considered necessary for different use cases. However, not all of them are useful for the current paper.
- 2.
Note that considering an event for dividing the traces implies that this selected event may not appear in the middle of a trace.
- 3.
Serviform is a Spanish BPO company with an IT consulting area.
- 4.
There are several alternatives for computing a fingerprint of an image, in this paper we based on [28].
References
IEEE standard for extensible event stream (XES) for achieving interoperability in event logs and event streams. IEEE Std 1849–2016, pp. 1–50 (2016)
van der Aalst, W.M.P.: Process Mining: Data Science in Action. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4
van der Aalst, W.M.P., Bichler, M., Heinzl, A.: Robotic process automation. Bus. Inf. Syst. Eng. 60(4), 269–272 (2018)
van der Aalst, W.: Formalization and verification of event-driven process chains. Inf. Softw. Technol. 41(10), 639–650 (1999)
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). https://doi.org/10.1007/978-3-319-66963-2_7
Asatiani, A., Penttinen, E.: Turning robotic process automation into commercial success - case opuscapita. J. Inf. Technol. Teach. Cases 6(2), 67–74 (2016)
Autoit (2018). https://www.autoitscript.com/site/autoit/. Accessed 1 Mar 2019
Cheng, H.J., Kumar, A.: Process mining on noisy logs - can log sanitization help to improve performance? Decis. Support. Syst. 79, 138–149 (2015)
Conforti, R., La Rosa, M., ter Hofstede, A.H.: Noise filtering of process execution logs based on outliers detection (2015)
Dev, H., Liu, Z.: Identifying frequent user tasks from application logs. In: Proceedings of the 22nd International Conference on Intelligent User Interfaces, pp. 263–273 (2017)
Dumais, S., Jeffries, R., Russell, D.M., Tang, D., Teevan, J.: Understanding user behavior through log data and analysis. In: Olson, J.S., Kellogg, W.A. (eds.) Ways of Knowing in HCI, pp. 349–372. Springer, New York (2014). https://doi.org/10.1007/978-1-4939-0378-8_14
Fung, H.P.: Criteria, use cases and effects of information technology process automation (ITPA). Adv. Robot. Autom. 3(3), 1–10 (2014)
Geyer-Klingeberg, J., Nakladal, J., Baldauf, F., Veit, F.: Process mining and robotic process automation: a perfect match. In: International Conference on Business Process Management, pp. 1–8 (2018)
Gusfield, D.: Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology. Cambridge University Press, Cambridge (1999)
Le Clair, C.: Digitization Leaders Share Robotic Process Automation Best Practices. Forrester Research Inc., Cambridge (2016)
Leno, V., Dumas, M., Maggi, F.M., La Rosa, M.: Multi-perspective process model discovery for robotic process automation. CEUR Work. Proc. 2114, 37–45 (2018)
Leopold, H., van der Aa, H., Reijers, H.A.: Identifying candidate tasks for robotic process automation in textual process descriptions. In: Gulden, J., Reinhartz-Berger, I., Schmidt, R., Guerreiro, S., Guédria, W., Bera, P. (eds.) BPMDS/EMMSAD -2018. LNBIP, vol. 318, pp. 67–81. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91704-7_5
Linn, C., Zimmermann, P., Werth, D.: Desktop activity mining - a new level of detail in mining business processes. In: Workshops der INFORMATIK 2018 - Architekturen, Prozesse, Sicherheit und Nachhaltigkeit, pp. 245–258 (2018)
Marrella, A., Catarci, T.: Measuring the learnability of interactive systems using a petri net based approach. In: Proceedings of the 2018 Designing Interactive Systems Conference, pp. 1309–1319. ACM (2018)
Măruşter, L., Weijters, A.T., Van Der Aalst, W.M., Van Den Bosch, A.: A rule-based approach for process discovery: dealing with noise and imbalance in process logs. Data Min. Knowl. Discov. 13(1), 67–87 (2006)
Rosa, M.L., Dumas, M., Uba, R., Dijkman, R.M.: Business process model merging: an approach to business process consolidation. ACM Trans. Softw. Eng. Methodol. (TOSEM) 22, 11 (2012)
Slaby, J.R.: Robotic automation emerges as a threat to traditional low-cost outsourcing. Horses for Sources (2018)
Spring cloud (2018). https://spring.io/projects/spring-cloud. Accessed 1 Mar 2019
Suriadi, S., Andrews, R., ter Hofstede, A.H., Wynn, M.T.: Event log imperfection patterns for process mining: towards a systematic approach to cleaning event logs. Inf. Syst. 64, 132–150 (2017)
Uipath (2018). http://www.uipath.com. Accessed 1 Mar 2019
Wang, G., Zhang, X., Tang, S., Zheng, H., Zhao, B.Y.: Unsupervised clickstream clustering for user behavior analysis. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 225–236. ACM (2016)
Willcocks, L., Lacity, M., Craig, A.: Robotic process automation: strategic transformation lever for global business services? J. Inf. Technol. Teach. Cases 7(1), 17–28 (2017)
Wong, C., Bern, M.W., Goldberg, D.: An image signature for any kind of image. In: International Conference on Image Processing, pp. 409–412 (2002)
Workfusion (2018). http://www.workfusion.com. Accessed 1 Mar 2019
Acknowledgments
This research has been supported by the Pololas project (TIN2016-76956-C3-2-R) of the Spanish Ministerio de Economía y Competitividad. Special thanks to Rafael Cabello from Serviform S.A. for providing his invaluable support and access to the case data.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Jimenez-Ramirez, A., Reijers, H.A., Barba, I., Del Valle, C. (2019). A Method to Improve the Early Stages of the Robotic Process Automation Lifecycle. In: Giorgini, P., Weber, B. (eds) Advanced Information Systems Engineering. CAiSE 2019. Lecture Notes in Computer Science(), vol 11483. Springer, Cham. https://doi.org/10.1007/978-3-030-21290-2_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-21290-2_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-21289-6
Online ISBN: 978-3-030-21290-2
eBook Packages: Computer ScienceComputer Science (R0)