Abstract
Workarounds can give valuable insights into the work processes that are carried out within organizations. To date, workarounds are usually identified using qualitative methods, such as interviews. We propose the semi-automated WORkaround Detection (SWORD) framework, which takes event logs as input. This extensible framework uses twenty-two patterns to semi-automatically detect workarounds. The value of the SWORD framework is that it can help to identify workarounds more efficiently and more thoroughly than is possible by the use of a more traditional, qualitative approach.
Through the use of real hospital data, we demonstrate the applicability and effectiveness of the SWORD framework in practice. We focused on the use of three patterns, which all turned out to be applicable to the characteristics of the data set. The use of two of these patterns also led to the identification of actual workarounds. Future work is geared to the extension of the patterns within the framework and the enhancement of techniques that can help to identify these in real-world data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
van der Aalst, W.M.P., van Dongen, B.F., Herbst, J., Maruster, L., Schimm, G., Weijters, A.J.M.M.: Workflow mining: a survey of issues and approaches. Data Knowl. Eng. 47(2), 237–267 (2003). https://doi.org/10.1016/s0169-023x(03)00066-1
Alter, S.: Theory of workarounds. Commun. Assoc. Inf. Syst. 34 (2014). https://doi.org/10.17705/1cais.03455
Azad, B., King, N.: Enacting computer workaround practices within a medication dispensing system. Eur. J. Inf. Syst. 17(3), 264–278 (2008). https://doi.org/10.1057/ejis.2008.14
Batista, E., Solanas, A.: Process mining in healthcare: a systematic review. In: 2018 9th International Conference on Information, Intelligence, Systems and Applications. IEEE, July 2018. https://doi.org/10.1109/iisa.2018.8633608
Beerepoot, I., Lu, X., van de Weerd, I., Reijers, H.A.: Seeing the signs of workarounds: a mixed-methods approach to the detection of nurses’ process deviations. In: Proceedings of the Annual Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences (2021). https://doi.org/10.24251/hicss.2021.456
Beerepoot, I., van de Weerd, I.: Prevent, redesign, adopt or ignore: improving healthcare using knowledge of workarounds. In: Twenty-Sixth European Conference on Information Systems (2018)
Bolt, A., de Leoni, M., van der Aalst, W.M.P.: Process variant comparison: using event logs to detect differences in behavior and business rules. Inf. Syst. 74, 53–66 (2018). https://doi.org/10.1016/j.is.2017.12.006
Borrego, D., Barba, I.: Conformance checking and diagnosis for declarative business process models in data-aware scenarios. Expert Syst. Appl. 41(11), 5340–5352 (2014). https://doi.org/10.1016/j.eswa.2014.03.010
Bose, R.P.J.C., van der Aalst, W.M.P.: Discovering signature patterns from event logs. In: 2013 IEEE Symposium on Computational Intelligence and Data Mining. IEEE, April 2013. https://doi.org/10.1109/cidm.2013.6597225
Burattin, A., Maggi, F.M., Sperduti, A.: Conformance checking based on multi-perspective declarative process models. Expert Syst. Appl. 65, 194–211 (2016). https://doi.org/10.1016/j.eswa.2016.08.040
Di Francescomarino, C., Ghidini, C., Maggi, F.M., Milani, F.: Predictive process monitoring methods: which one suits me best? In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNCS, vol. 11080, pp. 462–479. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98648-7_27
Dunzer, S., Stierle, M., Matzner, M., Baier, S.: Conformance checking: a state-of-the-art literature review. In: Proceedings of the 11th International Conference on Subject-Oriented Business Process Management. ACM Press (2019). https://doi.org/10.1145/3329007.3329014
Ejnefjäll, T., Ågerfalk, P.J.: Conceptualizing workarounds: meanings and manifestations in information systems research. Commun. Assoc. Inf. Syst. 45, 340–363 (2019). https://doi.org/10.17705/1cais.04520
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (1996)
Ignatiadis, I., Nandhakumar, J.: The effect of ERP system workarounds on organizational control: an interpretivist case study. Scand. J. Inf. Syst. 21(2), 3 (2009)
Ilie, V.: Psychological reactance and user workarounds. a study in the context of electronic medical records implementations. In: ECIS (2013)
Lauer, T., Rajagopalan, B.: Examining the relationship between acceptance and resistance in system implementation. In: AMCIS 2002 Proceedings, p. 179 (2002)
Lazovik, A., Aiello, M., Papazoglou, M.: Associating assertions with business processes and monitoring their execution. In: Proceedings of the 2nd International Conference on Service Oriented Computing. ACM Press (2004). https://doi.org/10.1145/1035167.1035182
de Leoni, M., van der Aalst, W.M.P.: Aligning event logs and process models for multi-perspective conformance checking: an approach based on integer linear programming. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 113–129. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40176-3_10
Lo, D., Cheng, H., Han, J., Khoo, S.C., Sun, C.: Classification of software behaviors for failure detection. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press (2009). https://doi.org/10.1145/1557019.1557083
Lu, X., Fahland, D., van den Biggelaar, F.J.H.M., van der Aalst, W.M.P.: Detecting deviating behaviors without models. In: Reichert, M., Reijers, H.A. (eds.) BPM 2015. LNBIP, vol. 256, pp. 126–139. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42887-1_11
Mahbub, K., Spanoudakis, G.: A framework for requirents monitoring of service based systems. In: Proceedings of the 2nd International Conference on Service Oriented Computing, pp. 84–93. ACM Press (2004). https://doi.org/10.1145/1035167.1035181
Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P.: Balanced multi-perspective checking of process conformance. Computing 98(4), 407–437 (2015). https://doi.org/10.1007/s00607-015-0441-1
Nguyen, H., Dumas, M., La Rosa, M., Maggi, F.M., Suriadi, S.: Business process deviance mining: review and evaluation. arXiv preprint arXiv:1608.08252, August 2016
Osuna, E., Freund, R., Girosi, F.: An improved training algorithm for support vector machines. In: Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop, pp. 276–285. IEEE (1997). https://doi.org/10.1109/nnsp.1997.622408
Ras, G., van Gerven, M., Haselager, P.: Explanation methods in deep learning: users, values, concerns and challenges. In: Escalante, H.J., et al. (eds.) Explainable and Interpretable Models in Computer Vision and Machine Learning. TSSCML, pp. 19–36. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98131-4_2
Rebuge, Á., Ferreira, D.R.: Business process analysis in healthcare environments: a methodology based on process mining. Inf. Syst. 37(2), 99–116 (2012). https://doi.org/10.1016/j.is.2011.01.003
Röder, N., Wiesche, M., Schermann, M.: A situational perspective on workarounds in IT-enabled business processes: a multiple case study. In: ECIS (2014)
Rozinat, A., van der Aalst, W.M.P.: Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33(1), 64–95 (2008). https://doi.org/10.1016/j.is.2007.07.001
Swennen, M., et al.: Capturing resource behaviour from event logs. In: SIMPDA, pp. 130–134 (2016)
Taymouri, F., La Rosa, M., Dumas, M., Maggi, F.M.: Business process variant analysis: survey and classification. Knowl. Based Syst. 211, 106557 (2021). https://doi.org/10.1016/j.knosys.2020.106557
Tucker, A.L.: The impact of workaround difficulty on frontline employees’ response to operational failures: a laboratory experiment on medication administration. Manag. Sci. 62(4), 1124–1144 (2016). https://doi.org/10.1287/mnsc.2015.2170
Weinzierl, S., Wolf, V., Pauli, T., Beverungen, D., Matzner, M.: Detecting workarounds in business processes-a deep learning method for analyzing event logs. In: Proceedings of the 28th European Conference on Information Systems, May 2020
Wynn, M.T., et al.: ProcessProfiler3D: a visualisation framework for log-based process performance comparison. Decis. Support Syst. 100, 93–108 (2017). https://doi.org/10.1016/j.dss.2017.04.004
Acknowledgments
This publication is part of the WorkAround Mining (WAM!) project (with project number 18490) which is (partly) financed by the Dutch Research Council (NWO).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
van der Waal, W., Beerepoot, I., van de Weerd, I., Reijers, H.A. (2022). The SWORD is Mightier Than the Interview: A Framework for Semi-automatic WORkaround Detection. 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_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-16103-2_9
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)