Abstract
Many algorithms for multi-criteria top-\(k\) query processing with ranking predicates have been proposed, but little effort has been directed toward genericity, i.e. supporting any type of access to the lists of predicate scores (sorted and/or random), or any access cost settings. In this paper we propose a general approach to exact and approximate generic top-\(k\) processing. 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. 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 for score refinement, then we compared it with specific top-\(k\) algorithms. In this paper, we propose two variants of existing generic strategies and experimentally compare them with the BR breadth-first strategy, showing that BR leads to better execution costs. We also extend the notion of \(\theta \)-approximation to the GF framework and present a first experimental study of the approximation potential of top-\(k\) algorithms on early stopping.
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Notes
- 1.
With random selection among candidates with the same score if necessary.
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Badr, M., Vodislav, D. (2015). Exact and Approximate Generic Multi-criteria Top-k Query Processing. In: Hameurlain, A., Küng, J., Wagner, R., Decker, H., Lhotska, L., Link, S. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XVIII. Lecture Notes in Computer Science(), vol 8980. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46485-4_3
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