FABIOLA: Towards the Resolution of Constraint Optimization Problems in Big Data Environment

Luisa Parody (), Ángel Jesús Varela Vaca (), Teresa Gómez López Mª () and Rafael M. Gasca ()
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Luisa Parody: Universidad de Sevilla
Ángel Jesús Varela Vaca: Universidad de Sevilla
Teresa Gómez López Mª: Universidad de Sevilla
Rafael M. Gasca: Universidad de Sevilla

A chapter in Advances in Information Systems Development, 2018, pp 113-127 from Springer

Abstract: Abstract The optimization problems can be found in several examples within companies, such as the minimization of the production costs, the faults produced, or the maximization of customer loyalty. The resolution of them is a challenge that entails an extra effort. In addition, many of today’s enterprises are encountering the Big Data problems added to these optimization problems. Unfortunately, to tackle this challenge by medium and small companies is extremely difficult or even impossible. In this paper, we propose a framework that isolates companies from how the optimization problems are solved. More specifically, we solve optimization problems where the data is heterogeneous, distributed and of a huge volume. FABIOLA (FAst BIg cOstraint LAb) framework enables to describe the distributed and structured data used in optimization problems that can be parallelized (the variables are not shared between the various optimization problems), and obtains a solution using Constraint Programming Techniques.

Keywords: Big data; Optimization problem; Constraint programming; Data structure (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-319-74817-7_8

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DOI: 10.1007/978-3-319-74817-7_8

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