Selective-NRA Algorithms for Top-k Queries
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 .
KeywordsRandom Access Aggregation Function Access Cost Good Competitor Cup98 Data
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