Proceedings of the 17th International Conference on Data Engineering
Stabbing the Sky: Efficient Skyline Computation over Sliding Windows
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Maximal vector computation in large data sets
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Maintaining Sliding Window Skylines on Data Streams
IEEE Transactions on Knowledge and Data Engineering
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Probabilistic skylines on uncertain data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Approaching the skyline in Z order
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Efficient sort-based skyline evaluation
ACM Transactions on Database Systems (TODS)
Efficient Maintaining of Skyline over Probabilistic Data Stream
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 04
Dominant Graph: An Efficient Indexing Structure to Answer Top-K Queries
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Probabilistic Skyline Operator over Sliding Windows
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Computing all skyline probabilities for uncertain data
Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Scalable skyline computation using object-based space partitioning
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Dynamic skylines considering range queries
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
Secured obfuscated queries in spatial networks
International Journal of Wireless and Mobile Computing
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Recently, some approaches of finding probabilistic skylines on uncertain data have been proposed. In these approaches, a data object is composed of instances, each associated with a probability. The probabilistic skyline is then defined as a set of non-dominated objects with probabilities exceeding or equaling a given threshold. In many applications, data are generated as a form of continuous data streams. Accordingly, we make the first attempt to study a problem of continuously returning probabilistic skylines over uncertain data streams in this paper. Moreover, the sliding window model over data streams is considered here. To avoid recomputing the probability of being not dominated for each uncertain object according to the instances contained in the current window, our main idea is to estimate the bounds of these probabilities for early determining which objects can be pruned or returned as results. We first propose a basic algorithm adapted from an existing approach of answering skyline queries on static and certain data, which updates these bounds by repeatedly processing instances of each object. Then, we design a novel data structure to keep dominance relation between some instances for rapidly tightening these bounds, and propose a progressive algorithm based on this new structure. Moreover, these two algorithms are also adapted to solve the problem of continuously maintaining top-k probabilistic skylines. Finally, a set of experiments are performed to evaluate these algorithms, and the experiment results reveal that the progressive algorithm much outperforms the basic one, directly demonstrating the effectiveness of our newly designed structure.