Incremental maintenance of topological patterns in spatial-temporal database

Yi Cheng Chen, Chao Ying Wu, Suh Yin Lee

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

2 Scopus citations

Abstract

Spatial temporal mining is an important research area with many interesting topics. Most spatial temporal databases are updating incrementally with time. Some discovered topological patterns may be invalidated and some new topological patterns may be introduced by the evolution of databases. However, the existing static algorithms are usually inefficient and not feasible to maintain topological patterns in an incremental environment. In this paper, we develop an efficient algorithm, Inc-TMiner (Incremental Topology Miner) to incrementally maintain topological patterns in spatial-temporal databases. The experimental results indicate that Inc-TMiner significantly outperforms state-of-the-art algorithms in execution time and possesses graceful scalability.

Original languageEnglish
Title of host publicationProceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
Pages853-860
Number of pages8
DOIs
StatePublished - 2011
Event11th IEEE International Conference on Data Mining Workshops, ICDMW 2011 - Vancouver, BC, Canada
Duration: 11 Dec 201111 Dec 2011

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Conference

Conference11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
Country/TerritoryCanada
CityVancouver, BC
Period11/12/1111/12/11

Keywords

  • Collocation pattern
  • Incremental mining
  • Spatial-temporal database
  • Topological pattern

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