A spatial time series dataset is a collection of time series, each referencing a location in a common spatial framework. Correlation analysis is often used to identify pairs of potentially interacting elements from the cross product of two spatial time series datasets. However, the computational cost of correlation analysis is very high when the dimension of the time series and the number of locations in the spatial frameworks are large. The key contribution of this paper is the use of spatial autocorrelation among spatial neighboring time series to reduce computational cost. A filter-and-refine algorithm based on coning, i.e. grouping of locations, is proposed to reduce the cost of correlation analysis over a pair of spatial time series datasets. Cone-level correlation computation can be used to eliminate (filter out) a large number of element pairs whose correlation is clearly below (or above) a given threshold. Element pair correlation needs to be computed for remaining pairs. Using experimental studies with Earth science datasets, we show that the filter-and-refine approach can save a large fraction of the computational cost, particularly when the minimal correlation threshold is high.
|Original language||English (US)|
|Title of host publication||Advances in Knowledge Discovery and Data Mining|
|Editors||Kyuseok Shim, Kyu-Young Wang, Jongwoo Jeon, Jaideep Srivastava|
|Number of pages||13|
|ISBN (Electronic)||3540047603, 9783540047605|
|State||Published - 2003|
|Event||7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003 - Seoul, Korea, Republic of|
Duration: Apr 30 2003 → May 2 2003
|Name||Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)|
|Other||7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003|
|Country/Territory||Korea, Republic of|
|Period||4/30/03 → 5/2/03|
Bibliographical notePublisher Copyright:
© Springer-Verlag Berlin Heidelberg 2003.