A framework for moving sensor data query and retrieval of dynamic atmospheric events

Authors:
Shen-Shyang Ho;Wenqing Tang;W. Timothy Liu;Markus Schneider
Affiliations:
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA;Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA;Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA;Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL
Venue:
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
Year:
2010

Citing 7
Cited 1

Extensible Parallel Query Processing for Exploratory Geoscientific Data Mining

Data Mining and Knowledge Discovery
Novel Approaches in Query Processing for Moving Object Trajectories

VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
SEB-tree: An Approach to Index Continuously Moving Objects

MDM '03 Proceedings of the 4th International Conference on Mobile Data Management
EOSDIS Petabyte Archives: Tenth Anniversary

MSST '05 Proceedings of the 22nd IEEE / 13th NASA Goddard Conference on Mass Storage Systems and Technologies
The Rise and Fall of CORBA

Queue - Component Technologies
Moving Objects Databases (The Morgan Kaufmann Series in Data Management Systems) (The Morgan Kaufmann Series in Data Management Systems)

Moving Objects Databases (The Morgan Kaufmann Series in Data Management Systems) (The Morgan Kaufmann Series in Data Management Systems)
Automated cyclone discovery and tracking using knowledge sharing in multiple heterogeneous satellite data

Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining

Tropical cyclone event sequence similarity search via dimensionality reduction and metric learning

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.