Spatiotemporal data mining in the era of big spatial data: Algorithms and applications

Ranga Raju Vatsavai, Varun Chandola, Scott Klasky, Auroop Ganguly, Anthony Stefanidis, Shashi Shekhar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

84 Scopus citations

Abstract

Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from the spatial and spatiotemporal data. However, explosive growth in the spatial and spatiotemporal data, and the emergence of social media and location sensing technologies emphasize the need for developing new and computationally efficient methods tailored for analyzing big data. In this paper, we review major spatial data mining algorithms by closely looking at the computational and I/O requirements and allude to few applications dealing with big spatial data.

Original languageEnglish (US)
Title of host publicationProceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2012
Pages1-10
Number of pages10
DOIs
StatePublished - Dec 1 2012
Event1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2012 - Redondo Beach, CA, United States
Duration: Nov 6 2012Nov 6 2012

Publication series

NameProceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2012
Volume1

Other

Other1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2012
CountryUnited States
CityRedondo Beach, CA
Period11/6/1211/6/12

Keywords

  • Big data
  • Computational and I/O challenges
  • Large scale data mining
  • Spatiotemporal patterns

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