dc.creator | Valencia Parra, Álvaro | es |
dc.creator | Varela Vaca, Ángel Jesús | es |
dc.creator | Parody Núñez, María Luisa | es |
dc.creator | Gómez López, María Teresa | es |
dc.date.accessioned | 2022-07-18T11:38:53Z | |
dc.date.available | 2022-07-18T11:38:53Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Valencia Parra, Á., Varela Vaca, Á.J., Parody Núñez, M.L. y Gómez López, M.T. (2020). Unleashing Constraint Optimisation Problem solving in Big Data environments. Journal of Computational Science, 45 (September 2020, art. nº 101180) | |
dc.identifier.issn | 1877-7503 | es |
dc.identifier.uri | https://hdl.handle.net/11441/135490 | |
dc.description.abstract | The application of the optimisation problems in the daily decisions of companies is able to be used for
finding the best management according to the necessities of the organisations. However, optimisation
problems imply a high computational complexity, increased by the current necessity to include a mas sive quantity of data (Big Data), for the creation of optimisation problems to customise products and
services for their clients. The irruption of Big Data technologies can be a challenge but also an impor tant mechanism to tackle the computational difficulties of optimisation problems, and the possibility to
distribute the problem performance. In this paper, we propose a solution that lets the query of a data
set supported by Big Data technologies that imply the resolution of Constraint Optimisation Problem
(COP). This proposal enables to: (1) model COPs whose input data are obtained from distributed and
heterogeneous data; (2) facilitate the integration of different data sources to create the COPs; and, (3)
solve the optimisation problems in a distributed way, to improve the performance. It is done by means
of a framework and supported by a tool capable of modelling, solving and querying the results of opti misation problems. The tool integrates the Big Data technologies and commercial solvers of constraint
programming. The suitability of the proposal and the development have been evaluated with real data
sets whose computational study and results are included and discussed | es |
dc.description.sponsorship | Ministerio de Ciencia y Tecnología RTI2018-094283-B-C33 | es |
dc.format | application/pdf | es |
dc.format.extent | 19 | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Journal of Computational Science, 45 (September 2020, art. nº 101180) | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Big Data | es |
dc.subject | Optimisation problem | es |
dc.subject | Constraint programming | es |
dc.subject | Distributed data | es |
dc.subject | Heterogeneous data format | es |
dc.title | Unleashing Constraint Optimisation Problem solving in Big Data environments | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | RTI2018-094283-B-C33 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S1877750320304816 | es |
dc.identifier.doi | 10.1016/j.jocs.2020.101180 | es |
dc.contributor.group | Universidad de Sevilla. TIC258: Data-centric Computing Research Hub | es |
dc.journaltitle | Journal of Computational Science | es |
dc.publication.volumen | 45 | es |
dc.publication.issue | September 2020, art. nº 101180 | es |
dc.contributor.funder | Ministerio de Ciencia Y Tecnología (MCYT). España | es |