Generic top-k query processing with breadth-first strategies

Abstract

Many algorithms for top-k query processing with ranking predicates have been proposed, but little effort has been directed toward genericity, i.e. supporting any type (sorted and/or random) or cost settings for the access to the lists of predicate scores. In previous work, we proposed BreadthRefine (BR), a generic algorithm that considers the current top-k candidates as a whole instead of focusing on the best candidate, then we compared it with specific top-k algorithms. In this paper, we compare the BR breadth-first strategy with other existing generic strategies and experimentally show that BR leads to better execution costs. To this end, we propose a general framework GF for generic top-k processing, able to express any top-k algorithm and present within this framework a first comparison between generic algorithms. We also extend the notion of θ-approximation to the GF framework and present a first experimental study of the approximation potential of top-k algorithms on early stopping. © 2013 Springer-Verlag.