Unleashing Constraint Optimisation Problem solving in Big Data environments
| Autor: | María Teresa Gómez-López, Luisa Parody, Ángel Jesús Varela-Vaca, Álvaro Valencia-Parra |
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| Přispěvatelé: | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos, Universidad de Sevilla. TIC258: Data-centric Computing Research Hub, Ministerio de Ciencia Y Tecnología (MCYT). España |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Big Data
Optimisation problem General Computer Science Computational complexity theory Computer science business.industry Distributed data Big data Heterogeneous data format 02 engineering and technology Resolution (logic) 01 natural sciences Industrial engineering 010305 fluids & plasmas Theoretical Computer Science Constraint (information theory) Data set Modeling and Simulation 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Constraint programming 020201 artificial intelligence & image processing business |
| ISSN: | 2018-0942 |
| Popis: | 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 Ministerio de Ciencia y Tecnología RTI2018-094283-B-C33 |
| Databáze: | OpenAIRE |
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