Constrained optimisation over massive databases

Constrained optimisation is increasingly considered by industry as a major candidate to cope with hard problems of practical relevance. However, the approach of exploiting, in those scenarios, current Constraint or Mathematical Programming solvers has severe limitations, which clearly demand new methods: data is usually stored in potentially very-large databases, and building a problem model in central memory suitable for current solvers could be very challenging or impossible. In this paper, we extend the approach followed in [3], by presenting a declarative language for constrained optimisation based on SQL, and novel techniques for local-search algorithms explicitly designed to handle massive data-sets. We also discuss and experiment with a solver implementation that, working on top of any DBMS, exploits such algorithms in a way transparent to the user, allowing smooth integration of constrained optimisation into industrial environments.