The problem of decentralized data sharing is relevant for a wide range of applications and is still a source of major theoretical and practical challenges, in spite of many years of sustained research in information integration. We focus on the challenge of efficiency of query evaluation in information-integration systems, with the objective of developing query-processing strategies that are widely applicable and easy to implement in real-life applications. In our algorithms we take into account important features of today's data-sharing applications, namely: XML as likely interface to or representation for data sources; potential for information overlap across data sources; and the need for inter-source processing (i.e., joins of data across data sources) in many applications.

Selecting and precomputing indexes and materialized views, with the goal of improving query-processing performance, is an important part of database-performance tuning. The significant complexity of the view- and index-selection problem may result in high total cost of ownership for database systems. In this paper, we develop efficient methods that deliver user-specified quality of the set of selected views and indexes when given view- and index-based plans as problem inputs. Here, quality means proximity to the globally optimum performance for the input query workload given the input query plans. Our experimental results and comparisons on synthetic and benchmark instances demonstrate the competitiveness of our approach and show that it provides a winning combination with end-to-end view- and index-selection frameworks such as those of [1, 2].

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.