Proceedings of the 17th International Conference on Data Engineering
An optimal and progressive algorithm for skyline queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Stabbing the Sky: Efficient Skyline Computation over Sliding Windows
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Maintaining Sliding Window Skylines on Data Streams
IEEE Transactions on Knowledge and Data Engineering
Continuous monitoring of top-k queries over sliding windows
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Sketching probabilistic data streams
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Estimating statistical aggregates on probabilistic data streams
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Efficient aggregation algorithms for probabilistic data
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Probabilistic skylines on uncertain data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Efficient computation of reverse skyline queries
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Monochromatic and bichromatic reverse skyline search over uncertain databases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Finding frequent items in probabilistic data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Sliding-window top-k queries on uncertain streams
Proceedings of the VLDB Endowment
A Framework for Clustering Uncertain Data Streams
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
On efficient reverse k-skyband query processing
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
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Given a set of data elements $\mathcal D$ in a d-dimensional space, a k-skyband query reports the set of elements which are dominated by at most k−1 other elements in $\mathcal D$. k-skyband query is a fundamental query type in data analyzing as it keeps a minimum candidate set for all top-k ranking queries where the ranking functions are monotonic. In this paper, we study the problem of k-skyband over uncertain data streams following the possible world semantics where each data element is associated with an occurrence probability. Firstly, a dynamic programming based algorithm is proposed to identify k-skyband results for a given set of uncertain elements regarding a pre-specified probability threshold. Secondly, we characterize the minimum set of elements to be kept in the sliding window to guarantee correct computing of k-skyband. Thirdly, efficient update techniques based on R-tree structures are developed to handle frequent updates of the elements over the sliding window. Extensive empirical studies demonstrate the efficiency and effectiveness of our techniques.