ACM Transactions on Database Systems (TODS)
Path skyline for moving objects
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Top-k similarity join over multi-valued objects
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
On efficient reverse k-skyband query processing
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Subspace global skyline query processing
Proceedings of the 16th International Conference on Extending Database Technology
Probabilistic skyline operator over sliding windows
Information Systems
Parallel computation of skyline and reverse skyline queries using mapreduce
Proceedings of the VLDB Endowment
On efficient reverse skyline query processing
Expert Systems with Applications: An International Journal
Parallel skyline queries over uncertain data streams in cloud computing environments
International Journal of Web and Grid Services
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In many applications involving the multiple criteria optimal decision making, users may often want to make a personal trade-off among all optimal solutions. As a key feature, the skyline in a multi-dimensional space provides the minimum set of candidates for such purposes by removing all points not preferred by any (monotonic) utility/scoring functions; that is, the skyline removes all objects not preferred by any user no mater how their preferences vary. Driven by many applications with uncertain data, the probabilistic skyline model is proposed to retrieve uncertain objects based on skyline probabilities. Nevertheless, skyline probabilities cannot capture the preferences of monotonic utility functions. Motivated by this, in this paper we propose a novel skyline operator, namely stochastic skyline. In the light of the expected utility principle, stochastic skyline guarantees to provide the minimum set of candidates for the optimal solutions over all possible monotonic multiplicative utility functions. In contrast to the conventional skyline or the probabilistic skyline computation, we show that the problem of stochastic skyline is NP-complete with respect to the dimensionality. Novel and efficient algorithms are developed to efficiently compute stochastic skyline over multi-dimensional uncertain data, which run in polynomial time if the dimensionality is fixed. We also show, by theoretical analysis and experiments, that the size of stochastic skyline is quite similar to that of conventional skyline over certain data. Comprehensive experiments demonstrate that our techniques are efficient and scalable regarding both CPU and IO costs.