A SciDB-based framework for efficient satellite data storage and query based on dynamic atmospheric event trajectory

Luboš Krčál, Shen Shyang Ho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Current research in climate informatics focuses mainly on the development of novel (machine learning, data mining, or statistical) techniques to analyze climate data (e.g. model, in-situ, or satellite) or to make prediction based on these climate data. One important component missing from this analysis workflow is data management that allows efficient and flexible data retrieval, (ease of) reproducibility, and the (ease of) techniques reuse on user-defined data subsets or other data. In this paper, we describe our preliminary investigation on the utilization of the distributed array-based database management system, SciDB, to support data-driven climate science research. We focus on modeling and generating indices that allow effective execution of various spatiotemporal queries on satellite data. Moreover, we demonstrate fast and accurate data retrieval based on user-specified trajectories from the SciDB database containing tropical cyclone trajectories and the complete ten-year QuikSCAT ocean surface wind fields satellite data. Our preliminary work indicates the feasibility of the array-based technology for multiple satellite data storage, query, and analysis. Towards this end, a successful deployment of SciDB-based data storage can facilitate the use of data from multiple satellites for climate and weather research.

Original languageEnglish (US)
Title of host publicationProceedings of the 4th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2015
EditorsVarun Chandola, Ranga Raju Vatsavai
PublisherAssociation for Computing Machinery, Inc
Pages7-14
Number of pages8
ISBN (Electronic)9781450339742
DOIs
StatePublished - Nov 3 2015
Event4th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2015 - Seattle, United States
Duration: Nov 3 2015 → …

Publication series

NameProceedings of the 4th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2015

Other

Other4th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2015
CountryUnited States
CitySeattle
Period11/3/15 → …

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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    Krčál, L., & Ho, S. S. (2015). A SciDB-based framework for efficient satellite data storage and query based on dynamic atmospheric event trajectory. In V. Chandola, & R. R. Vatsavai (Eds.), Proceedings of the 4th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2015 (pp. 7-14). (Proceedings of the 4th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2015). Association for Computing Machinery, Inc. https://doi.org/10.1145/2835185.2835190