Subsumption and indexing in constraint query languages with linear arithmetic constraints

Abstract

Bottom-up evaluation of a program-query pair in a constraint query language (CQL) starts with the facts in the database and repeatedly applies the rules of the program, in iterations, to compute new facts, until we have reached a fixpoint. Checking if a fixpoint has been reached amounts to checking if any “new” facts were computed in an iteration. Such a check also enhances efficiency in that subsumed facts can be discarded, and not be used to make any further derivations in subsequent iterations, if we use Semi-naive evaluation. We show that the problem of subsumption in CQLs with linear arithmetic constraints is co-NP complete, and present a deterministic algorithm, based on the divide and conquer strategy, for this problem. We also identify polynomial-time sufficient conditions for subsumption and non-subsumption in CQLs with linear arithmetic constraints. We adapt indexing strategies from spatial databases for efficiently indexing facts in such a CQL: such indexing is crucial for performance in the presence of large databases. Based on a recent algorithm by C. Lassez and J.-L. Lassez for quantifier elimination, we present an incremental version of the algorithm to check for subsumption in CQLs with linear arithmetic constraints.