TY - GEN
T1 - A framework for moving sensor data query and retrieval of dynamic atmospheric events
AU - Ho, Shen Shyang
AU - Tang, Wenqing
AU - Liu, W. Timothy
AU - Schneider, Markus
N1 - Funding Information:
This work was partially carried out at the Jet Propulsion Laboratory, California Institute of Technology and was funded by the National Aeronautics and Space Adminstration (NASA) Advanced Information Systems Technology (AIST) Program under grant number AIST-08-0081.
PY - 2010
Y1 - 2010
N2 - One challenge in Earth science research is the accurate and efficient ad-hoc query and retrieval of Earth science satellite sensor data based on user-defined criteria to study and analyze atmospheric events such as tropical cyclones. The problem can be formulated as a spatio-temporal join query to identify the spatio-temporal location where moving sensor objects and dynamic atmospheric event objects intersect, either precisely or within a user-defined proximity. In this paper, we describe an efficient query and retrieval framework to handle the problem of identifying the spatio-temporal intersecting positions for satellite sensor data retrieval. We demonstrate the effectiveness of our proposed framework using sensor measurements from QuikSCAT (wind field measurement) and TRMM (precipitation vertical profile measurements) satellites, and the trajectories of the tropical cyclones occurring in the North Atlantic Ocean in 2009.
AB - One challenge in Earth science research is the accurate and efficient ad-hoc query and retrieval of Earth science satellite sensor data based on user-defined criteria to study and analyze atmospheric events such as tropical cyclones. The problem can be formulated as a spatio-temporal join query to identify the spatio-temporal location where moving sensor objects and dynamic atmospheric event objects intersect, either precisely or within a user-defined proximity. In this paper, we describe an efficient query and retrieval framework to handle the problem of identifying the spatio-temporal intersecting positions for satellite sensor data retrieval. We demonstrate the effectiveness of our proposed framework using sensor measurements from QuikSCAT (wind field measurement) and TRMM (precipitation vertical profile measurements) satellites, and the trajectories of the tropical cyclones occurring in the North Atlantic Ocean in 2009.
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U2 - 10.1007/978-3-642-13818-8_9
DO - 10.1007/978-3-642-13818-8_9
M3 - Conference contribution
AN - SCOPUS:77955039308
SN - 3642138179
SN - 9783642138171
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 96
EP - 113
BT - Scientific and Statistical Database Management - 22nd International Conference, SSDBM 2010, Proceedings
T2 - 22nd International Conference on Scientific and Statistical Database Management, SSDBM 2010
Y2 - 30 June 2010 through 2 July 2010
ER -