id: 05293855
dt: a
an: 05293855
au: Su, Liang; Zou, Peng; Jia, Yan
ti: Adaptive mining the approximate skyline over data stream.
so: Shi, Yong (ed.) et al., Computational science ‒ ICCS 2007. 7th
    international conference, Beijing, China, May 27‒30, 2007.
    Proceedings, Part III. Berlin: Springer (ISBN 978-3-540-72587-9/pbk).
    Lecture Notes in Computer Science 4489, 742-745 (2007).
py: 2007
pu: Berlin: Springer
la: EN
cc: 
ut: Approximate skyline; adaptive algorithm; data stream; data mining
ci: 
li: doi:10.1007/978-3-540-72588-6_122
ab: Summary: 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.
rv: