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

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

2013

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.

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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