A Data-adaptive Trace Abstraction Approach to the Prediction of Business Process Performances

Antonio Bevacqua, Marco Carnuccio, Francesco Folino, Massimo Guarascio, Luigi Pontieri

Abstract

This paper presents a novel approach to the discovery of predictive process models, which are meant to support the run-time prediction of some performance indicator (e.g., the remaining processing time) on new ongoing process instances. To this purpose, we combine a series of data mining techniques (ranging from pattern mining, to non-parametric regression and to predictive clustering) with ad-hoc data transformation and abstraction mechanisms. As a result, a modular representation of the process is obtained, where different performance-relevant variants of it are provided with separate regression models, and discriminated on the basis of context information. Notably, the approach is capable to look at the given log traces at a proper level of abstraction, in a pretty automatic and transparent fashion, which reduces the need for heavy intervention by the analyst (which is, indeed, a major drawback of previous solutions in the literature). The approach has been validated on a real application scenario, with satisfactory results, in terms of both prediction accuracy and robustness.

References

  1. Agrawal, R. and Srikant, R. (1994). Fast algorithms for mining association rules in large databases. In Proc. of 20th Int. Conf. on Very Large Data Bases (VLDB'94), pages 487-499.
  2. Blockeel, H. and Raedt, L. D. (1998). Top-down induction of first-order logical decision trees. Artificial Intelligence, 101(1-2):285-297.
  3. DLAI Group (1998). CLUS: A predictive clustering system. Available at http://dtai.cs.kuleuven.be/clus/.
  4. Draper, N. R. and Smith, H. (1998). Applied Regression Analysis. Wiley Series in Probability and Statistics.
  5. Folino, F., Guarascio, M., and Pontieri, L. (2012). Discovering context-aware models for predicting business process performances. In Proc. of 20th Int. Conf. on Cooperative Information Systems (CoopIS'12), pages 287-304.
  6. Frank, E., Hall, M. A., Holmes, G., Kirkby, R., and Pfahringer, B. (2005). Weka - a machine learning workbench for data mining. In The Data Mining and Knowledge Discovery Handbook, pages 1305-1314.
  7. Hardle, W. and Mammen, E. (1993). Comparing nonparametric versus parametric regression fits. The Annals of Statistics, 21(4):1926-1947.
  8. Harlde, W. (1990). Applied NonParametric Regression. Cambridge University Press.
  9. Quinlan, R. J. (1992). Learning with continuous classes. In In Proc. of 5th Australian Joint Conference on Artificial Intelligence (AI'92), pages 343-348.
  10. van der Aalst, W. M. P. and et al. (2007). ProM 4.0: Comprehensive support for real process analysis. In Proc. of 28th Int. Conf. on Applications and Theory of Petri Nets and Other Models of Concurrency (ICATPN'07), pages 484-494.
  11. van der Aalst, W. M. P., Schonenberg, M. H., and Song, M. (2011). Time prediction based on process mining. Information Systems, 36(2):450-475.
  12. van der Aalst, W. M. P., van Dongen, B. F., Herbst, J., Maruster, L., Schimm, G., and Weijters, A. J. M. M. (2003). Workflow mining: a survey of issues and approaches. Data & Knowledge Engineering, 47(2):237-267.
  13. van Dongen, B. F., Crooy, R. A., and van der Aalst, W. M. P. (2008). Cycle time prediction: When will this case finally be finished? In Proc. of 16th Int. Conf. on Cooperative Information Systems (CoopIS'08), pages 319-336.
  14. Witten, I. H. and Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques, Second Edition. Morgan Kaufmann Publishers Inc.
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Paper Citation


in Harvard Style

Bevacqua A., Carnuccio M., Folino F., Guarascio M. and Pontieri L. (2013). A Data-adaptive Trace Abstraction Approach to the Prediction of Business Process Performances . In Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-8565-59-4, pages 56-65. DOI: 10.5220/0004448700560065


in Bibtex Style

@conference{iceis13,
author={Antonio Bevacqua and Marco Carnuccio and Francesco Folino and Massimo Guarascio and Luigi Pontieri},
title={A Data-adaptive Trace Abstraction Approach to the Prediction of Business Process Performances},
booktitle={Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2013},
pages={56-65},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004448700560065},
isbn={978-989-8565-59-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - A Data-adaptive Trace Abstraction Approach to the Prediction of Business Process Performances
SN - 978-989-8565-59-4
AU - Bevacqua A.
AU - Carnuccio M.
AU - Folino F.
AU - Guarascio M.
AU - Pontieri L.
PY - 2013
SP - 56
EP - 65
DO - 10.5220/0004448700560065