@inproceedings{5b8f0b278fc248f392db2a02b8765714,
title = "Development of a Cloud-based Data Match-Up Service (CDMS) in Support of Ocean Science Applications",
abstract = "There is a need in the oceanographic community for a generalized data collocation capability for satellite and/or in situ observations that is publicly accessible and provides flexibility and reproducibility for use cases ranging from open science to satellite retrieval calibration and validation. The Cloud-based Data Match-Up Service (CDMS) is a collaborative effort between NASA JPL, COAPS, NCAR, and Saildrone to address this need. With an exponential increase in the volume of satellite data, the CDMS architecture is designed to be scalable and to leverage the elasticity of the cloud. The differences between remote sensing data at various data processing levels and the heterogeneous nature of in situ data makes developing a generic system challenging, but CDMS is designed with the consideration of making it possible to efficiently onboard new datasets. This paper describes the architecture and technologies behind CDMS and provides details on how the data match-up implementation is validated.",
keywords = "big data, cloud, collocation, data match-up, in situ, oceanography, parallel compute, remote sensing, satellite",
author = "Nga Chung and Thomas Cram and Smith, \{Shawn R.\} and Tsontos, \{Vardis M.\} and Thomas Huang and Kimberly Sparling and Stepheny Perez and Wai Phyo and Zaihua Ji and Riley Kuttruff",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 OCEANS Hampton Roads, OCEANS 2022 ; Conference date: 17-10-2022 Through 20-10-2022",
year = "2022",
doi = "10.1109/OCEANS47191.2022.9977163",
language = "English",
series = "Oceans Conference Record (IEEE)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "OCEANS 2022 Hampton Roads",
address = "United States",
}