Query evaluation using overlapping views: completeness and efficiency

Abstract

We study the problem of finding efficient equivalent view-based rewritings of relational queries, focusing on query optimization using materialized views under the assumption that base relations cannot contain duplicate tuples. A lot of work in the literature addresses the problems of answering queries using views and query optimization. However, most of it proposes solutions for special cases, such as for conjunctive queries (CQs) or for aggregate queries only. In addition, most of it addresses the problems separately under set or bag-set semantics for query evaluation, and some of it proposes heuristics without formal proofs for completeness or soundness. In this paper we look at the two problems by considering CQ/A queries - that is, both pure conjunctive and aggregate queries, with aggregation functions SUM, COUNT, MIN, and MAX; the DISTINCT keyword in (SQL versions of) our queries is also allowed. We build on past work to provide algorithms that handle this general setting. This is possible because recent results on rewritings of CQ/A queries [1, 8] show that there are sound and complete algorithms based on containment tests of CQs.Our focus is that our algorithms are efficient as well as sound and complete. Besides the contribution we make in putting and addressing the problems in this general setting, we make two additional contributions for bag-set and set semantics. First, we propose efficient sound and complete tests for equivalence of CQ/A queries to rewritings that use overlapping views (the algorithms are complete with respect to the language of rewritings). These results apply not only to query optimization, but to all areas where the goal is to obtain efficient equivalent view-based query rewritings. Second, based on these results we propose two sound algorithms, BDPV and CDPV, that find efficient execution plans for CQ/A queries in terms of materialized views. Both algorithms extend the cost-based query-optimization approach of System R [19]. The efficient sound algorithm BDPV is also complete in some cases, whereas CDPV is sound and complete for all CQ/A queries we consider. We present a study of the completeness-efficiency tradeoff in the algorithms, and provide experimental results that show the viability of our approach and test the limits of query optimization using overlapping views.