IN51B
Big Data in Earth Science: From Hype to Reality I Posters

Friday, 18 December 2015: 08:00-12:20
Poster Hall (Moscone South)
Primary Conveners:  Kwo-Sen Kuo, NASA Goddard SFC, Greenbelt, MD, United States; Bayesics, LLC, Bowie, MD, United States
Conveners:  Rahul Ramachandran, NASA Marshall Space Flight Center, Huntsville, AL, United States, Ben James Kingston Evans, Australian National University, Canberra, Australia and Mike M Little, NASA Goddard SFC, Greenbelt, MD, United States
Chairs:  Mike M Little, NASA Goddard SFC, Greenbelt, MD, United States and Rahul Ramachandran, NASA Marshall Space Flight Center, Huntsville, AL, United States
OSPA Liaisons:  Kwo-Sen Kuo, Earth System Science Interdisciplinary Center, COLLEGE PARK, MD, United States
 
AppEEARS: Simple and Intuitive Access to Analysis Ready Data (79162)
Robert Quenzer, Organization Not Listed, Washington, DC, United States
 
Supporting Research using Satellite Data: A Framework for Spatiotemporal Queries in SciDB (84514)
Shen Shyang Ho and Lubos Krcal, Nanyang Technological University, Singapore, Singapore
 
Parallel and Scalable Big Data Analysis in the Earth Sciences with JuML (78553)
Markus Goetz, Organization Not Listed, Washington, DC, United States
 
Exploring the impact of big data in economic geology using cloud-based synthetic sensor networks (66811)
Jess Robertson, CSIRO, Minerals Resources National Research Flagship, Perth, Australia and Jens F Klump, CSIRO Earth Science and Resource Engineering Perth, Perth, WA, Australia
 
The Ophidia Stack: Toward Large Scale, Big Data Analytics Experiments for Climate Change (75278)
Sandro Fiore1, Dean Norman Williams2, Alessandro D'Anca3, Paola Nassisi3 and Giovanni Aloisio3, (1)CMCC Salento, Lecce, Italy, (2)Lawrence Livermore National Laboratory, Livermore, CA, United States, (3)Euro Mediterranean Centre on Climate Change, Lecce, Italy
 
Big Data and Data Models for Climate System Energetics (84339)
David W Fillmore, Tech-X Corporation, Boulder, CO, United States
 
The Feasibility of Predicting Nino 3.4 Index Using a Sparse Approximation Algorithm (71494)
Xiao Peng1, Tiejian Li1, Yuantao Gu2 and Ailing Zhang2, (1)Tsinghua University, State Key Laboratory of Hydroscience and Hydraulic Engineering, Beijing, China, (2)Tsinghua University, Department of Electronic Engineering, Beijing, China
 
Unleashing spatially distributed ecohydrology modeling using Big Data tools (73697)
Brian Miles, University of North Carolina at Chapel Hill, Geography, Chapel Hill, NC, United States and Ray Idaszak, Renaissance Computing Institute, Chapel Hill, NC, United States
 
Mining and Utilizing Dataset Relevancy from Oceanographic Dataset (MUDROD) Metadata, Usage Metrics, and User Feedback to Improve Data Discovery and Access (75575)
Yongyao Jiang, George Mason University Fairfax, Fairfax, VA, United States
 
Application of Open Source Technologies for Oceanographic Data Analysis (75071)
Michael Gangl1, Thomas Huang1, Nga T Quach1, Brian D Wilson2, George Chang1, Edward M Armstrong1 and Toshio Michael Chin2, (1)NASA Jet Propulsion Laboratory, Pasadena, CA, United States, (2)Jet Propulsion Laboratory, Pasadena, CA, United States
 
The Hurricane Problem – The Three Faces of the Big Data Challenges (84532)
Svetla M Hristova-Veleva1, Mark Boothe2, Sundararaman Gopalakrishnan3, Ziad S Haddad1, Brian Knosp4, Bjorn Lambrigtsen1, Peggy Li1, Michael t Montgomery2, Noppasin Niamsuwan1, Tsae-Pyng J Shen1, Vijay Tallapragada5, Simone Tanelli4, Samuel Trahan5, Francis J Turk1 and Quoc A Vu4, (1)NASA Jet Propulsion Laboratory, Pasadena, CA, United States, (2)Naval Postgraduate School, Monterey, CA, United States, (3)Atlantic Oceanographic and Meteorological Laboratory, Hurricane Research Division, Miami, FL, United States, (4)Jet Propulsion Laboratory, Pasadena, CA, United States, (5)NOAA College Park, College Park, MD, United States
 
Evaluation of Big Data Containers for Popular Storage, Retrieval, and Computation Primitives in Earth Science Analysis (83595)
Kamalika Das, NASA - Ames Research Center, Mountain View, CA, United States, Thomas Clune, NASA Goddard Space Flight Center, Greenbelt, MD, United States, Kwo-Sen Kuo, Earth System Science Interdisciplinary Center, COLLEGE PARK, MD, United States, Chris A Mattmann, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States, Thomas Huang, NASA Jet Propulsion Laboratory, Pasadena, CA, United States, Daniel Duffy, NASA Center for Climate Simulation, Greenbelt, MD, United States, Chaowei Phil Yang, George Mason University Fairfax, Fairfax, VA, United States, Ted Habermann, HDF Group, Champaign, IL, United States and AIST Data Container Study Team
 
A Restricted Boltzman Neural Net to Infer Carbon Uptake from OCO-2 Satellite Data (80375)
Milton Halem1, John Dorband1, Asen Radov1, Mariama Barr-Dallas1, Pierre Gentine2 and NASA AIST QAC Team, (1)University of Maryland Baltimore County, Computer Science, Baltimore, MD, United States, (2)Columbia University of New York, Palisades, NY, United States
 
Large-Scale, Multi-Sensor Atmospheric Data Fusion Using Hybrid Cloud Computing (84635)
Brian D Wilson, Jet Propulsion Laboratory, Pasadena, CA, United States
 
SciDB versus Spark: A Preliminary Comparison Based on an Earth Science Use Case (79489)
Thomas Clune1, Kwo-Sen Kuo2, Khoa Doan3 and Amidu Oloso1, (1)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (2)Earth System Science Interdisciplinary Center, COLLEGE PARK, MD, United States, (3)University of Maryland College Park, College Park, MD, United States
 
SciSpark's SRDD : A Scientific Resilient Distributed Dataset for Multidimensional Data (85241)
Rahul Sunil Palamuttam, University of California San Diego, La Jolla, CA, United States
 
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