Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/2271

Venn Sampling: A Novel Prediction Technique for Moving Objects

Authors Tao, Y.
Zhai, J.
Papadias, D.
Li, Q.
Issue Date 2005
Source Proceedings - International Conference on Data Engineering, 2005, p. 680-691
Summary Given a region q<sub>R</sub> and a future timestamp q<sub>T</sub>, a "range aggregate" query estimates the number of objects expected to appear in q<sub>R</sub> at time q<sub>T</sub>. Currently the only methods for processing such queries are based on spatio-temporal histograms, which have several serious problems. First, they consume considerable space in order to provide accurate estimation. Second, they incur high evaluation cost. Third, their efficiency continuously deteriorates with time. Fourth, their maintenance requires significant update overhead. Motivated by this, we develop Venn sampling (VS), a novel estimation method optimized for a set of "pivot queries" that reflect the distribution of actual ones. In particular, given m pivot queries, VS achieves perfect estimation with only O(m) samples, as opposed to O(2<sup>m</sup>) required by the current state of the art in workload-aware sampling. Compared with histograms, our technique is much more accurate (given the same space), produces estimates with negligible cost, and does not deteriorate with time. Furthermore, it permits the development of a novel "query-driven" update policy, which reduces the update cost of conventional policies significantly. © 2005 IEEE.
Subjects
ISSN 1084-4627
Rights © 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Language English
Format Conference paper
Access View full-text via Scopus
Find@HKUST
Files in this item:
File Description Size Format
ICDE05VS.pdf 209.66 kB Adobe PDF