Fast Spatio-Temporal Data Mining of Large Geophysical Datasets

Fast Spatio-Temporal Data Mining of Large Geophysical Datasets (1995)

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by P. Stolorz , H. Nakamura , E. Mesrobian , R. R. Muntz , J. R. Santos , J. Yi , K. Ng

Venue:	In Proceedings of the First International Conference on Knowledge Discovery and Data Mining
Citations:	26 - 2 self

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Datum	Value	Source
TITLE	Fast Spatio-Temporal Data Mining of Large Geophysical Datasets	INFERENCE
AUTHOR NAME	P. Stolorz	SVM HeaderParse 0.1
AUTHOR AFFIL	Jet Propulsion Laboratory Dept. of Earth and Planetary Physics; California Institute of Technology University of Tokyo	SVM HeaderParse 0.2
AUTHOR NAME	H. Nakamura	SVM HeaderParse 0.1
AUTHOR AFFIL	Jet Propulsion Laboratory Dept. of Earth and Planetary Physics; California Institute of Technology University of Tokyo	SVM HeaderParse 0.2
AUTHOR NAME	E. Mesrobian	SVM HeaderParse 0.1
AUTHOR AFFIL	Computer Science Dept., UCLA Atmospheric Sciences Dept., UCLA	SVM HeaderParse 0.2
AUTHOR NAME	R. R. Muntz	SVM HeaderParse 0.1
AUTHOR AFFIL	Computer Science Dept., UCLA Atmospheric Sciences Dept., UCLA	SVM HeaderParse 0.2
AUTHOR NAME	J. R. Santos	SVM HeaderParse 0.1
AUTHOR AFFIL	Computer Science Dept., UCLA Atmospheric Sciences Dept., UCLA	SVM HeaderParse 0.2
AUTHOR NAME	J. Yi	SVM HeaderParse 0.1
AUTHOR AFFIL	Computer Science Dept., UCLA Atmospheric Sciences Dept., UCLA	SVM HeaderParse 0.2
AUTHOR NAME	K. Ng	SVM HeaderParse 0.1
AUTHOR AFFIL	Computer Science Dept., UCLA Atmospheric Sciences Dept., UCLA	SVM HeaderParse 0.2
ABSTRACT	The important scientific challenge of understanding global climate change is one that clearly requires the application of knowledge discovery and datamining techniques on a massive scale. Advances in parallel supercomputing technology, enabling high-resolution modeling, as well as in sensor technology, allowing data capture on an unprecedented scale, conspire to overwhelm present-day analysis approaches. We present here early experiences with a prototype exploratory data analysis environment, CONQUEST, designed to provide content-based access to such massive scientific datasets. CONQUEST (CONtent-based Querying in Space and Time) employs a combination of workstations and massively parallel processors (MPP's) to mine geophysical datasets possessing a prominent temporal component. It is designed to enable complex multi-modal interactive querying and knowledge discovery, while simultaneously coping with the extraordinary computational demands posed by the scope of the datasets involved. A...	user correction - Legacy Corrections
YEAR	1995	INFERENCE
VENUE	In Proceedings of the First International Conference on Knowledge Discovery and Data Mining	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	300--305	INFERENCE
CITATIONS	10 found	ParsCit 1.0