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

Authors:
Ranga Raju Vatsavai;Auroop Ganguly;Varun Chandola;Anthony Stefanidis;Scott Klasky;Shashi Shekhar
Affiliations:
Oak Ridge National Laboratory, Oak Ridge, TN;Northeastern University, Boston, Massachusetts;Oak Ridge National Laboratory, Oak Ridge, TN;George Mason University, Fairfax VA;Oak Ridge National Laboratory, Oak Ridge, TN;University of Minnesota, Minneapolis, MN
Venue:
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
Year:
2012

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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.