Abstract
Data extraction and preparation are the most time-consuming phases of any process mining project. Due to the variability on the sources of event data, it remains a highly manual process in most of the cases. Moreover, it is very difficult to obtain reliable event data in enterprise systems that are not process-aware. Some techniques, like redo log process mining, try to solve these issues by automating the process as much as possible, and enabling event extraction in systems that are not process aware. This paper presents the challenges faced by redo log, and traditional process mining, comparing both approaches at theoretical and practical levels. Finally, we demonstrate that the data obtained with redo log process mining in a real-life environment is, at least, as valid as the one extracted by the traditional approach.
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Watson, H.J., Wixom, B.H.: The current state of business intelligence. Computer 40(9), 96–99 (2007). https://doi.org/10.1109/MC.2007.331
Ingvaldsen, J.E., Gulla, J.A.: Preprocessing support for large scale process mining of SAP transactions. In: ter Hofstede, A., Benatallah, B., Paik, H.-Y. (eds.) BPM 2007. LNCS, vol. 4928, pp. 30–41. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78238-4_5
Roest, A.: A practitioner’s guide for process mining on ERP systems: the case of SAP order to cash. Master’s thesis, Technische Universiteit Eindhoven, The Netherlands (2012)
Segers, I.: Investigating the application of process mining for auditing purposes. Master’s thesis, Technische Universiteit Eindhoven, The Netherlands (2007)
Yano, K., Nomura, Y., Kanai, T.: A practical approach to automated business process discovery. In: 2013 17th IEEE International Enterprise Distributed Object Computing Conference Workshops (EDOCW), pp. 53–62, September 2013
Verbeek, H.M.W., Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: XES, XESame, and ProM 6. In: Soffer, P., Proper, E. (eds.) CAiSE Forum 2010. LNBIP, vol. 72, pp. 60–75. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-17722-4_5
de Murillas, E.G.L., van der Aalst, W.M.P., Reijers, H.A.: Process mining on databases: unearthing historical data from redo logs. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) BPM 2015. LNCS, vol. 9253, pp. 367–385. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23063-4_25
Hoogendoorn, G.E.: A comparative study for process mining approaches in a real-life environment. Master’s thesis, Eindhoven University of Technology (2017)
Jans, M.J.: From relational database to valuable event logs for process mining purposes: a procedure. Technical report, Hasselt University (2017)
de Murillas, E.G.L., Reijers, H.A., van der Aalst, W.M.P.: Connecting databases with process mining: a meta model and toolset. In: Schmidt, R., Guédria, W., Bider, I., Guerreiro, S. (eds.) BPMDS/EMMSAD -2016. LNBIP, vol. 248, pp. 231–249. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39429-9_15
van Eck, M.L., Lu, X., Leemans, S.J.J., van der Aalst, W.M.P.: PM\(^2\): a process mining project methodology. In: Zdravkovic, J., Kirikova, M., Johannesson, P. (eds.) CAiSE 2015. LNCS, vol. 9097, pp. 297–313. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19069-3_19
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González López de Murillas, E., Hoogendoorn, G.E., Reijers, H.A. (2018). Redo Log Process Mining in Real Life: Data Challenges & Opportunities. In: Teniente, E., Weidlich, M. (eds) Business Process Management Workshops. BPM 2017. Lecture Notes in Business Information Processing, vol 308. Springer, Cham. https://doi.org/10.1007/978-3-319-74030-0_45
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DOI: https://doi.org/10.1007/978-3-319-74030-0_45
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