Efficient mining of skyline objects in subspaces over data streams |
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Authors: | Zhenhua Huang Shengli Sun Wei Wang |
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Affiliation: | 1. Department of Computer Science, Tongji University, Shanghai, China 2. School of Software and Microelectronics, Peking University, Beijing, China 3. Department of Computing and Information Technology, Fudan University, Shanghai, China
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Abstract: | Given a set of k-dimensional objects, the skyline query finds the objects that are not dominated by others. In practice, different users may
be interested in different dimensions of the data, and issue queries on any subset of k dimensions in stream environments.
This paper focuses on supporting concurrent and unpredictable subspace skyline queries over data streams. Simply to compute
and store the skyline objects of every subspace in stream environments will incur expensive update cost. To balance the query
cost and update cost, we only maintain the full space skyline in this paper. We first propose an efficient maintenance algorithm
and several novel pruning techniques. Then, an efficient and scalable two-phase algorithm is proposed to process the skyline
queries in different subspaces based on the full space skyline. Furthermore, we present the theoretical analyses and extensive
experiments that demonstrate our method is both efficient and effective. |
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