Abstract
Process mining serves for gaining insights into business process behavior based on event logs. These techniques are typically limited to addressing data included in the log. Recent studies suggest extracting data-rich event logs from databases or transaction logs. However, these event logs are at a very fine granularity level, substituting business-level activities by low-level database operations, and challenging data-aware process mining. To address this gap, we propose an approach that enables a broad and deep exploration of process behavior, using a conceptual framework based on three sources: the event log that holds information regarding the business-level activities, the (relational) database that stores the current values of data elements, and the transaction (redo) log that captures historical data operations performed on the database as a result of business process activities. Nine types of operations define how to map subsets of elements among the three sources in order to support human analysts in exploring and understanding the reasons of observed process behavior. A preliminary evaluation analyzes the outcomes for four useful scenarios.
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This definition abstracts from low level implementation aspects such as keys and foreign keys.
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References
Bezerra, F., Wainer, J., van der Aalst, W.M.P.: Anomaly detection using process mining. In: Halpin, T., Krogstie, J., Nurcan, S., Proper, E., Schmidt, R., Soffer, P., Ukor, R. (eds.) BPMDS/EMMSAD -2009. LNBIP, vol. 29, pp. 149–161. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01862-6_13
Calvanese, D., Kalayci, T.E., Montali, M., Tinella, S.: Ontology-based data access for extracting event logs from legacy data: the onprom tool and methodology. In: Abramowicz, W. (ed.) BIS 2017. LNBIP, vol. 288, pp. 220–236. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59336-4_16
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
De Leoni, M., van der Aalst, W.M.:. Data-aware process mining: discovering decisions in processes using alignments. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing, pp. 1454–1461. ACM, March 2013
de Murillas, E.G.L., Reijers, H.A., Van Der Aalst, W.M.: Connecting databases with process mining: a meta model and toolset. Softw. Syst. Modeling (2018). https://doi.org/10.1007/s10270-018-0664-7
Dyer, J.N., Rogers, C.: Adapting the access northwind database to support a database course. J. Inf. Syst. Educ. 26(2), 85 (2015)
Hand, D.J.: Principles of data mining. Drug Saf. 30(7), 621–622 (2007)
Jans, M., Soffer, P.: From relational database to event log: decisions with quality impact. In: Teniente, E., Weidlich, M. (eds.) BPM 2017. LNBIP, vol. 308, pp. 588–599. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74030-0_46
Ramezani, E., Fahland, D., van der Aalst, W.M.P.: Where did i misbehave? Diagnostic information in compliance checking. In: Barros, A., Gal, A., Kindler, E. (eds.) BPM 2012. LNCS, vol. 7481, pp. 262–278. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32885-5_21
Rozinat, A., van der Aalst, W.M.P.: Decision mining in ProM. In: Dustdar, S., Fiadeiro, J.L., Sheth, A.P. (eds.) BPM 2006. LNCS, vol. 4102, pp. 420–425. Springer, Heidelberg (2006). https://doi.org/10.1007/11841760_33
Taghiabadi, E.R., Gromov, V., Fahland, D., van der Aalst, W.M.P.: Compliance checking of data-aware and resource-aware compliance requirements. In: Meersman, R., Panetto, H., Dillon, T., Missikoff, M., Liu, L., Pastor, O., Cuzzocrea, A., Sellis, T. (eds.) OTM 2014. LNCS, vol. 8841, pp. 237–257. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45563-0_14
Tsoury, A., Soffer, P., Reinhartz-Berger, I.: Towards impact analysis of data in business processes. In: Schmidt, R., Guédria, W., Bider, I., Guerreiro, S. (eds.) BPMDS/EMMSAD -2016. LNBIP, vol. 248, pp. 125–140. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39429-9_9
Van der Aalst, W.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer-Verlag, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19345-3
Aalst, W.M.P.: Extracting event data from databases to unleash process mining. In: vom Brocke, J., Schmiedel, T. (eds.) BPM - Driving Innovation in a Digital World. MP, pp. 105–128. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-14430-6_8
Van der Aalst, W., Adriansyah, A., van Dongen, B.: Replaying history on process models for conformance checking and performance analysis. Wiley Interdiscip. Rev.: Data Min. Knowl. Discov. 2(2), 182–192 (2012)
Winslett, M., Smith, K., Qian, X.: Formal query languages for secure relational databases. ACM Trans. Database Syst. (TODS) 19(4), 626–662 (1994)
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This research is supported by the Israel Science Foundation under grant 856/13.
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Tsoury, A., Soffer, P., Reinhartz-Berger, I. (2018). A Conceptual Framework for Supporting Deep Exploration of Business Process Behavior. In: Trujillo, J., et al. Conceptual Modeling. ER 2018. Lecture Notes in Computer Science(), vol 11157. Springer, Cham. https://doi.org/10.1007/978-3-030-00847-5_6
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