Accurate and Transparent Path Prediction Using Process Mining

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ID Serval
serval:BIB_943845D011CF
Type
Partie de livre
Sous-type
Chapitre: chapitre ou section
Collection
Publications
Institution
Titre
Accurate and Transparent Path Prediction Using Process Mining
Titre du livre
Advances in Databases and Information Systems
Auteur⸱e⸱s
Bernard Gaël, Andritsos Periklis
Editeur
Springer International Publishing
ISBN
9783030287290
9783030287306
ISSN
0302-9743
1611-3349
Statut éditorial
Publié
Date de publication
2019
Pages
235-250
Langue
anglais
Résumé
Anticipating the next events of an ongoing series of activities has many compelling applications in various industries. It can be used to improve customer satisfaction, to enhance operational efficiency, and to streamline health-care services, to name a few. In this work, we propose an algorithm that predicts the next events by leveraging business process models obtained using process mining techniques. Because we are using business process models to build the predictions, it allows business analysts to interpret and alter the predictions. We tested our approach with more than 30 synthetic datasets as well as 6 real datasets. The results have superior accuracy compared to using neural networks while being orders of magnitude faster.
Création de la notice
01/10/2019 12:17
Dernière modification de la notice
02/10/2019 6:08
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