The problem of obtaining efficient answers to top-k queries has attracted a lot of research attention. Several algorithms and numerous variants of the top-k retrieval problem have been introduced in recent years. The general form of this problem requests the k highest ranked values from a relation, using monotone combining functions on (a subset of) its attributes. In this paper we explore space performance tradeoffs related to this problem. In particular we study the problem of answering top-k queries using views. A view in this context is a materialized version of a previously posed query, requesting a number of highest ranked values according to some monotone combining function defined on a subset of the attributes of a relation. Several problems of interest arise in the presence of such views. We start by presenting a new algorithm capable of combining the information from a number of views to answer ad hoc top-k queries. We then address the problem of identifying the most promising (in terms of performance) views to use for query answering in the presence of a collection of views. We formalize both problems and present efficient algorithms for their solution. We also discuss several extensions of the basic problems in this setting.

We present the results of a thorough experimental study that deploys our techniques on real and synthetic data sets. Our results indicate that the techniques proposed herein comprise a robust solution to the problem of top-k query answering using views, gracefully exploring the space versus performance tradeoffs in the context of top-k query answering.