RT Journal Article
T1 A Vector-Based Classification Approach for Remaining Time Prediction in Business Processes
A1 Aburomman, Ahmad Abdel Karim Ali
A1 Lama Penín, Manuel
A1 Bugarín-Diz, Alberto
K1 Encoding
K1 Hidden Markov models
K1 Predictive models
K1 Feature extraction
K1 Monitoring
K1 Proposals
K1 Business processes enhancement
K1 Predictive business process monitoring
K1 Business processes management
K1 Business intelligence
AB In this paper, we deal with one of the current challenges in process mining enhancement: the prediction of remaining times in business processes. Accurate predictions of the remaining time, defined as the required time for an instance process to finish, are critical in many systems for organizations being able to establish a priori requirements, for optimal management of resources or for improving the quality of the services organizations provide. Our approach consists of i) extracting and assessing a number of features on the business logs, that provide a structural characterization of the traces; ii) extending the well-known annotated transition system (ATS) model to include these features; iii) proposing a partitioning strategy for the lists of features associated to each state in the extended ATS; and iv) applying a linear regression technique to each partition for predicting the remaining time of new traces. Extensive experimentation using eight attributes and ten real-life datasets show that the proposed approach outperforms in terms of mean absolute error and accuracy all the other approaches in state of the art, which includes ATS-based, non-ATS based as well as Deep Learning-based approaches
PB IEEE
YR 2019
FD 2019
LK http://hdl.handle.net/10347/21968
UL http://hdl.handle.net/10347/21968
LA eng
NO A. Aburomman, M. Lama and A. Bugarín, "A Vector-Based Classification Approach for Remaining Time Prediction in Business Processes," in IEEE Access, vol. 7, pp. 128198-128212, 2019
NO This work was supported in part by the Spanish Ministry for Science, Innovation, and Universities under Grant TIN2017-84796-C2-1-R, and in part by the Galician Ministry of Education, University, and Professional Training under Grant ED431C 2018/29 and ‘‘accreditation 2016-2019, Grant ED431G/08.’’ All grants were co-funded by the European Regional Development Fund (ERDF/FEDER program)
DS Minerva
RD 24 abr 2026