Dimensionality reduction for long duration and complex spatio-temporal queries

Ghazi Al-Naymat, Sanjay Chawla, Joachim Gudmundsson

Research output: Chapter in Book/Report/Conference proceedingChapter

30 Citations (Scopus)

Abstract

In this paper we present an approach to mine and query spatio-temporal data with the aim of finding interesting patterns and understanding the underlying data generating process. An important class of queries is based on the flock pattern. A flock is a large subset of objects moving along paths close to each other for a certain pre-defined time. One approach to process a "flock query" is to map spatio-temporal data into a high dimensional space and reduce the query into a sequence of standard range queries which can be presented using a spatial indexing structure. However, as is well known, the performance of spatial indexing structures drastically deteriorates in high dimensional space. In this paper we propose a preprocessing strategy which consists of using a random projection to reduce the dimensionality of the transformed space. Our experimental results show, for the first time, the possibility of breaking the curse of dimensionality in a spatio-temporal setting.

Original languageEnglish
Title of host publicationProceedings of the ACM Symposium on Applied Computing
Pages393-397
Number of pages5
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2007 ACM Symposium on Applied Computing - Seoul
Duration: 11 Mar 200715 Mar 2007

Other

Other2007 ACM Symposium on Applied Computing
CitySeoul
Period11/3/0715/3/07

Keywords

  • Data mining
  • Dimensionality reduction
  • Spatio-temporal data

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Al-Naymat, G., Chawla, S., & Gudmundsson, J. (2007). Dimensionality reduction for long duration and complex spatio-temporal queries. In Proceedings of the ACM Symposium on Applied Computing (pp. 393-397) https://doi.org/10.1145/1244002.1244095

Dimensionality reduction for long duration and complex spatio-temporal queries. / Al-Naymat, Ghazi; Chawla, Sanjay; Gudmundsson, Joachim.

Proceedings of the ACM Symposium on Applied Computing. 2007. p. 393-397.

Research output: Chapter in Book/Report/Conference proceedingChapter

Al-Naymat, G, Chawla, S & Gudmundsson, J 2007, Dimensionality reduction for long duration and complex spatio-temporal queries. in Proceedings of the ACM Symposium on Applied Computing. pp. 393-397, 2007 ACM Symposium on Applied Computing, Seoul, 11/3/07. https://doi.org/10.1145/1244002.1244095
Al-Naymat G, Chawla S, Gudmundsson J. Dimensionality reduction for long duration and complex spatio-temporal queries. In Proceedings of the ACM Symposium on Applied Computing. 2007. p. 393-397 https://doi.org/10.1145/1244002.1244095
Al-Naymat, Ghazi ; Chawla, Sanjay ; Gudmundsson, Joachim. / Dimensionality reduction for long duration and complex spatio-temporal queries. Proceedings of the ACM Symposium on Applied Computing. 2007. pp. 393-397
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