Venn sampling: A novel prediction technique for moving objects

Yufei Tao, Jian Zhai, Dimitris Papadias, Qing Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

19 Citations (Scopus)

Abstract

Given a region qR and a future timestamp qT, a "range aggregate" query estimates the number of objects expected to appear in qR at time qT. 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(2m) 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.

Original languageEnglish
Title of host publicationProceedings - 21st International Conference on Data Engineering, ICDE 2005
Pages680-691
Number of pages12
DOIs
Publication statusPublished - 12 Dec 2005
Externally publishedYes
Event21st International Conference on Data Engineering, ICDE 2005 - Tokyo, Japan
Duration: 5 Apr 20058 Apr 2005

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627

Conference

Conference21st International Conference on Data Engineering, ICDE 2005
Country/TerritoryJapan
CityTokyo
Period5/04/058/04/05

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

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