Abstract
Efficient processing of top-k queries has become a classical research area recently since it has lots of application fields. Fagin et al. proposed the “middleware cost” for a top-k query algorithm. In some databases there is no way to perform a random access, Fagin et al. proposed NRA (No Random Access) algorithm for this case. In this paper, we provided some key observations of NRA. Based on them, we proposed a new algorithm called Selective-NRA (SNRA) which is designed to minimize the useless access of a top-k query. However, we proved the SNRA is not instance optimal in Fagin’s notion and we also proposed an instance optimal algorithm Hybrid-SNRA based on algorithm SNRA. We conducted extensive experiments on both synthetic and real-world data. The experiments showed SNRA (Hybrid-SNRA) has less access cost than NRA. For some instances, SNRA performed 50% fewer accesses than NRA .
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Yuan, J., Sun, GZ., Tian, Y., Chen, G., Liu, Z. (2009). Selective-NRA Algorithms for Top-k Queries. In: Li, Q., Feng, L., Pei, J., Wang, S.X., Zhou, X., Zhu, QM. (eds) Advances in Data and Web Management. APWeb WAIM 2009 2009. Lecture Notes in Computer Science, vol 5446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00672-2_4
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DOI: https://doi.org/10.1007/978-3-642-00672-2_4
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