Computational geometry: an introduction
Computational geometry: an introduction
On the Average Number of Maxima in a Set of Vectors and Applications
Journal of the ACM (JACM)
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Proceedings of the 17th International Conference on Data Engineering
Efficient Progressive Skyline Computation
Proceedings of the 27th International Conference on Very Large Data Bases
An optimal and progressive algorithm for skyline queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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The efficiency of skyline query processing has recently received a lot of attention in database community. However, researchers often ignore that the skyline set will be beyond control in the applications which must deal with enormous data set. Consequently, it is not useful for users at all. In this paper, we propose a novel skyline reducing algorithm, i.e. SRANF. SRANF algorithm adopts the technique of noise filtering. It filters skyline noises directly on the original data set based on the acceptable difference, and returns the objects which can not be filtered from the original data set. Furthermore, our experiment demonstrated that SRANF is both efficient and effective.