dc.contributor
Universitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.contributor
Universitat Politècnica de Catalunya. ALBCOM - Algorismia, Bioinformàtica, Complexitat i Mètodes Formals
dc.contributor
Universitat Politècnica de Catalunya. GPLN - Grup de Processament del Llenguatge Natural
dc.contributor.author
Delicado Alcántara, Luis
dc.contributor.author
Carmona Vargas, Josep
dc.contributor.author
Padró, Lluís
dc.date.issued
2020-09-28
dc.identifier
Delicado, L.; Carmona, J.; Padró, L. Flexible process model mapping using relaxation labeling. "Fundamenta informaticae", 28 Setembre 2020, vol. 175, núm. 1-4, p. 123-141.
dc.identifier
https://hdl.handle.net/2117/330656
dc.identifier
10.3233/FI-2020-1950
dc.description.abstract
Computing a mapping between two process models is a crucial technique, since it enables reasoning and operating across processes, like providing a similarity score between two processes, or merging different process variants to generate a consolidated process model. In this paper we present a new flexible technique for process model mapping, based on the relaxation labeling constraint satisfaction algorithm. The technique can be instantiated so that different modes are devised, depending on the context. For instance, it can be adapted to the case where one of the mapped process models is incomplete, or it can be used to ground an adaptable similarity measure between process models. The approach has been implemented inside the open platform NLP4BPM, providing a visualization of the performed mappings and computed similarity scores. The experimental results witness the flexibility and usefulness of the technique proposed.
dc.description.abstract
This work has been supported by MINECO and FEDER funds under grant TIN2017-86727-C2-1-R.
dc.description.abstract
Peer Reviewed
dc.description.abstract
Postprint (author's final draft)
dc.format
application/pdf
dc.relation
https://content.iospress.com/articles/fundamenta-informaticae/fi1950
dc.relation
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-86727-C2-1-R/ES/MODELOS Y METODOS BASADOS EN GRAFOS PARA LA COMPUTACION EN GRAN ESCALA/
dc.subject
Àrees temàtiques de la UPC::Informàtica::Informàtica teòrica
dc.subject
Constraint satisfaction (Artificial intelligence)
dc.subject
Process modeling
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Model similarity
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Relaxation labeling
dc.subject
Grafs, Teoria de
dc.title
Flexible process model mapping using relaxation labeling
