A top-N selection query against a relation is to find the N tuples that satisfy the query condition the best but not necessarily completely. In this paper, we propose a new method for evaluating top-N selection queries against relational databases. This method employs a learning-based strategy. Initially, it finds and saves the optimal search spaces for a small number of random top-N queries. The learned knowledge is then used to evaluate new queries. Extensive experiments are carried out to measure the performance of this strategy and the results indicate that it is highly competitive with existing techniques for both low-dimensional and high-dimensional data. Furthermore, the knowledge base can be updated based on new user queries to reflect new query patterns so that frequently submitted queries can be processed most efficiently.
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