Dimensionality reduction for long duration and complex spatio-temporal queries

Ghazi Al-Naymat, Sanjay Chawla, Joachim Gudmundsson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

31 Scopus citations


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 2007 ACM Symposium on Applied Computing
PublisherAssociation for Computing Machinery
Number of pages5
ISBN (Print)1595934804, 9781595934802
StatePublished - 2007
Externally publishedYes
Event2007 ACM Symposium on Applied Computing - Seoul, Korea, Republic of
Duration: Mar 11 2007Mar 15 2007

Publication series

NameProceedings of the ACM Symposium on Applied Computing


Conference2007 ACM Symposium on Applied Computing
Country/TerritoryKorea, Republic of


  • Data mining
  • Dimensionality reduction
  • Spatio-temporal data


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