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 sortie 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 observatioris 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.