Adaptive Mining the Approximate Skyline over Data Stream

Authors:
Liang Su;Peng Zou;Yan Jia
Affiliations:
School of Computer Science National University of Defense Technology, Changsha 410073, China;School of Computer Science National University of Defense Technology, Changsha 410073, China;School of Computer Science National University of Defense Technology, Changsha 410073, China
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Year:
2007

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Abstract

Skyline queries, which return the objects that are better than or equal in all dimensions and better in at least one dimension, are useful in many decision making and monitor applications. With the number of dimensions increasing and continuous large volume data arriving, mining the approximate skylines over data stream under control of losing quality is a more meaningful problem. In this paper, firstly, we propose a novel concept, called approximate skyline. Then, an algorithm is developed which prunes the skyline objects within the acceptable difference and adopts correlation coefficient to adjust adaptively approximate query quality. Furthermore, our experiments show that the proposed methods are both efficient and effective.