Database Reference
In-Depth Information
plex system. Understanding and monitoring earth's phenomena and
processes require the development of novel analysis tools and techniques
that are cognizant of unique data-centric issues and challenges specific
to the earth science domain, such as spatial heterogeneity, multi-scale
nature and uncertainty. Earth science applications using sensor datasets
broadly include (i) event detection either on land, ocean or atmosphere,
e.g. monitoring land cover changes using vegetation data, and identify-
ing ocean eddy dynamics using altimeter data, (ii) relationship mining
between spatio-temporal attributes and events, e.g. discovering atmo-
spheric teleconnections such as climate dipoles. With the advancing rate
of earth science sensor data acquisition technologies both at larger tem-
poral and spatial scales, as well as the advances in computational tools
and techniques, earth science research offers fertile grounds for acceler-
ated knowledge discovery about the earth's complex system at large.
7. Acknowledgments
This work was supported in part by the National Science Foundation
under Grants IIS-1029711 and IIS-0905581, an NSF Graduate Research
Fellowship, an NSF Nordic Research Opportunity, and the Norwegian
National Research Council as well as the Planetary Skin Institute. Ac-
cess to computing facilities was provided by the University of Minnesota
Supercomputing Institute.
References
[1] Atmospheric InfraRed Sounder v5 (AIRS).
http://disc.
[2] S. Boriah, V. Kumar, M. Steinbach, C. Potter, and S. Klooster.
Land cover change detection: A case study. In
KDD '08: Proceedings
of the 14th ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining
, pages 857-865. ACM, 2008.
[3] S. Boriah, V. Kumar, M. Steinbach, P.-N. Tan, C. Potter, and
S. Klooster. Detecting ecosystem disturbances and land cover
change using data mining. In H. Kargupta, J. Han, P. Yu, R. Mot-
wani, and V. Kumar, editors,
Next Generation of Data Mining
.
CRC Press, 2009.
[4] S. Boriah, V. Mithal, A. Garg, V. Kumar, M. Steinbach, C. Pot-
ter, and S. Klooster. A comparative study of algorithms for land
cover change. In
CIDU'10: Proceedings of Annual Conference on
Intelligent Data Understanding
, pages 175-188, October 2010.
