Abstract
This paper presents a framework for analyzing and predicting the performances of a business process, based on historical data gathered during its past enactments. The framework hinges on an inductive-learning technique for discovering a special kind of predictive process models, which can support the run-time prediction of a given performance measure (e.g., the remaining processing time/steps) for an ongoing process instance, based on a modular representation of the process, where major performance-relevant variants of it are modeled with different regression models, and discriminated on the basis of context variables. The technique is an original combination of different data mining methods (ranging from pattern mining, to non-parametric regression and predictive clustering) and ad-hoc data transformation mechanisms, allowing for looking at the log traces at a proper level of abstraction, in a pretty automatic and transparent way. The technique has been integrated in a performance monitoring architecture, meant to provide managers and analysts (and possibly the process enactment environment) with continuously updated performance statistics, as well as with the anticipated notification of likely SLA violations. The approach has been validated on a real-life case study, with satisfactory results, in terms of both prediction accuracy and robustness.
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Notes
- 1.
Notice that similar trends of behavior were discovered for the mae and mape metrics.
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)
van der Aalst, W.M.P., et al.: ProM 4.0: comprehensive support for Real process analysis. In: Kleijn, J., Yakovlev, A. (eds.) ICATPN 2007. LNCS, vol. 4546, pp. 484–494. Springer, Heidelberg (2007)
van der Aalst, W.M.P., Schonenberg, M.H., Song, M.: Time prediction based on process mining. Inf. Syst. 36(2), 450–475 (2011)
Blockeel, H., Raedt, L.D.: Top-down induction of first-order logical decision trees. Artif. Intell. 101(1–2), 285–297 (1998)
Conforti, R., Fortino, G., La Rosa, M., ter Hofstede, A.H.M.: History-aware, real-time risk detection in business processes. In: Meersman, R., et al. (eds.) OTM 2011, Part I. LNCS, vol. 7044, pp. 100–118. Springer, Heidelberg (2011)
DLAI Group: CLUS: a predictive clustering system (1998). http://dtai.cs.kuleuven.be/clus/
van Dongen, B.F., Crooy, R.A., van der Aalst, W.M.P.: Cycle time prediction: when will this case finally be finished? In: Meersman, R., Tari, Z. (eds.) OTM 2008, Part I. LNCS, vol. 5331, pp. 319–336. Springer, Heidelberg (2008)
Draper, N.R., Smith, H.: Applied Regression Analysis. Wiley Series in Probability and Statistics. Wiley, New York (1998)
Folino, F., Guarascio, M., Pontieri, L.: Discovering context-aware models for predicting business process performances. In: Meersman, R., et al. (eds.) OTM 2012, Part I. LNCS, vol. 7565, pp. 287–304. Springer, Heidelberg (2012)
Frank, E., Hall, M.A., Holmes, G., Kirkby, R., Pfahringer, B.: Weka - a machine learning workbench for data mining. In: Maimon, O., Rokach, L. (eds.) The Data Mining and Knowledge Discovery Handbook, pp. 1305–1314. Springer, New York (2005)
Hardle, W., Mammen, E.: Comparing nonparametric versus parametric regression fits. Ann. Stat. 21(4), 1926–1947 (1993)
Harlde, W.: Applied NonParametric Regression. Cambridge University Press, Boston (1990)
Quinlan, R.J.: Learning with continuous classes. In: Proceedings of 5th Australian Joint Conference on Artificial Intelligence (AI’92), pp. 343–348 (1992)
Schonenberg, H., Weber, B., van Dongen, B.F., van der Aalst, W.M.P.: Supporting flexible processes through recommendations based on history. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 51–66. Springer, Heidelberg (2008)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann Publishers Inc., San Francisco (2005)
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Bevacqua, A., Carnuccio, M., Folino, F., Guarascio, M., Pontieri, L. (2014). A Data-Driven Prediction Framework for Analyzing and Monitoring Business Process Performances. In: Hammoudi, S., Cordeiro, J., Maciaszek, L., Filipe, J. (eds) Enterprise Information Systems. ICEIS 2013. Lecture Notes in Business Information Processing, vol 190. Springer, Cham. https://doi.org/10.1007/978-3-319-09492-2_7
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