An offline optimal SPARQL query planning approach to evaluate online heuristic planners

Abstract

In graph databases, a given graph query can be executed in a large variety of semantically equivalent ways. Each such execution plan produces the same results, but at different computation costs. The query planning problem consists of finding, for a given query, an execution plan with the minimum cost. The traditional greedy or heuristic cost-based approaches addressing the query planning problem do not guarantee by design the optimality of the chosen execution plan. In this paper, we present a principled framework to solve the query planning problem by casting it into an Integer Linear Programming problem, and discuss its applications to testing and improving heuristic-based query planners.