A Scalable Algorithm for Answering Top-K Queries Using Cached Views

Recently, various algorithms were proposed to speed up top-k query answering by using multiple materialized query results. Nevertheless, for most of the proposed algorithms, a potentially costly view selection operation is required. In fact, the processing cost has been shown to be linear with respect to the number of views and can be exorbitant given the large number of views to be considered. In this paper, we address the problem of identifying the top-N promising views to use for top-k query answering in the presence of a collection of views. We propose a novel algorithm, called Top-N rewritings algorithm, for handling this problem, which aims to achieve significant reduction in query execution time. Indeed, it considers minimal amount of rewritings that are likely necessary to return the top-k tuples for a top-k query. We consider, also, the problem of how, efficiently, exploit the output of the Top-N rewritings algorithm to retrieve the top-k tuples through two possible solutions. The results of a thorough experimental study indicate that the proposed algorithm offers a robust solution to the problem of efficient top-k query answering using views since it discards non-promising query rewritings from the view selection process.