Evaluation of a Stream of Top-N Selection Queries in Relational Databases

Abstract

In relational databases and their applications, an important issue is to evaluate a stream of top-N selection queries. For this issue, we propose a new method with learning-based strategies and region clustering techniques in this paper. This method uses a knowledge base to store related information of some past queries, groups the search regions of the past queries into larger regions and retrieves the tuples from the larger regions. To answer a newly submitted query, our method tries to obtain most results from the previously retrieved tuples that are still in main memory. Thus, this method seeks to minimize the response time by reducing the search regions or avoiding accesses to the underlying databases. Extensive experiments are carried out to measure the performance of this new strategy and the results indicate that it is significantly better than the naive method for both low-dimensional and high-dimensional data.