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Evaluating Top-k Skyline Queries on R-Trees
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Author(s): Marlene Goncalves (Universidad Simón Bolívar, Venezuela), Fabiana Reggio (Universidad Simón Bolívar, Venezuela)and Krisvely Varela (Universidad Simón Bolívar, Venezuela)
Copyright: 2016
Pages: 39
Source title:
Handbook of Research on Innovative Database Query Processing Techniques
Source Author(s)/Editor(s): Li Yan (Nanjing University of Aeronautics and Astronautics, China)
DOI: 10.4018/978-1-4666-8767-7.ch003
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Abstract
The Skyline queries retrieve a set of data whose elements are incomparable in terms of multiple user-defined criteria. In addition, Top-k Skyline queries filter the best k Skyline points where k is the number of answers desired by the user. Several index-based algorithms have been proposed for the evaluation of Top-k Skyline queries. These algorithms make use of indexes defined on a single attribute and they require an index for each user-defined criterion. In traditional databases, the use of multidimensional indices has shown that may improve the performance of database queries. In this chapter, three pruning criteria were defined and several algorithms were developed to evaluate Top-k Skyline queries. The proposed algorithms are based on a multidimensional index, pruning criteria and the strategies Depth First Search and Breadth First Search. Finally, an experimental study was conducted in this chapter to analyze the performance and answer quality of the proposed algorithms.
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