[Go-essp-tech] FW: Workshop on Data and Computational Science Technologies for Earth Science Research - IEEE Big Data Conference

[Mattmann, Chris A (3980)]

Also wanted to share this one, co-located at the same conference,
Dean:

http://geo-bigdata.github.io/


Cheers!
Chris

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Chris Mattmann, Ph.D.
Chief Architect
Instrument Software and Science Data Systems Section (398)
NASA Jet Propulsion Laboratory Pasadena, CA 91109 USA
Office: 168-519, Mailstop: 168-527
Email: chris.a.mattmann at nasa.gov
WWW:  http://sunset.usc.edu/~mattmann/
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Adjunct Associate Professor, Computer Science Department
University of Southern California, Los Angeles, CA 90089 USA
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-----Original Message-----
From: <Williams>, "Dean N." <williams13 at llnl.gov>
Date: Wednesday, May 27, 2015 at 5:31 PM
To: ESGF Development Team <esgf-devel at llnl.gov>, PCMDI CDAT
software	development <uvcdat-devel at llnl.gov>, "go-essp-tech at ucar.edu"
<go-essp-tech at ucar.edu>
Subject: [Go-essp-tech] FW: Workshop on Data and Computational Science
Technologies for Earth Science Research - IEEE Big Data Conference

>FYI…
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>Best regards,
>Dean
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>Workshop on Data and Computational Science Technologies for Earth Science
>Research
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>2015 IEEE
> Big Data Conference <http://cci.drexel.edu/bigdata/bigdata2015/>October
>29, 2015 – November 1, 2015Santa Clara,
>CAhttp://ieee-bigdata-earthscience.jpl.nasa.gov
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>Workshop Description
>Currently, the analysis of large data collections from earth science
>research is executed through traditional computational and data analysis
>approaches, which require users to bring data to their desktops
> and perform local data analysis. Future earth science remote sensing
>missions, which historically assume that all data can be collected,
>transmitted, processed, and archived, may not scale as more capable
>instruments stress existing architectural approaches
> and systems. A new paradigm is needed in order to increase the
>productivity and effectiveness of scientific data analysis. This paradigm
>must recognize that architectural and analytical choices are
>interrelated, and must be carefully coordinated in any system
> that aims to allow efficient, interactive scientific exploration and
>discovery to exploit massive data collections, from point of collection
>(e.g., onboard) to analysis and decision support. Both future
>observational systems, including satellite and airborne
> experiments, and research in climate modeling will significantly
>increase the size of the data requiring new approaches across the entire
>data lifecycle from capture to generation, management, and analysis of
>the data.
>The workshop seeks computational and data science experts to present on
>their research and discuss Big Data roadmaps, architectures,
>technologies, and methodologies for future Earth Science data challenges
> emerging from both observational systems and climate studies.
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>Technical Focus 
>I. Architectural considerations/tradeoffs for integrating the entire data
>lifecycle from observational systems to climate modeling and research
>•   Approaches for scaling observing systems for satellite, airborne and
>ground-based sensors
>•   Integration of computational methods with observing systems
>•   New concepts for data intensive missions
>•   Integration of data, computing/HPC/cloud, and algorithms
>•   Cloud computing, software as a service
>•   New technologies (e.g., distributed frameworks, database
>technologies, search, etc) for scaling the architecture
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>II. Onboard/Sensor-based Computing
>•   Embedded and real-time data reduction and triage/analytics methods
>•   Managing bandwidth constraints for high volume instruments
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>III. Scalable Data Management and Computation for ground-based systems
>•           Capturing well-architected and curated data repositories
>•           Data and semantic information architectures
>•           Architecting automated pipelines for data capture
>•           Enabling analytics on data pipelines for computation, data
>discovery, event detection, reduction, etc
>•           Open source data science frameworks
>•           Cloud computing
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>IV. Scalable Data Analytics for Massively Distributed Data
>•         Access and integration of highly distributed, heterogeneous data
>•         Novel statistical approaches for data integration and fusion
>•         Sampling strategies from massive data repositories
>•         Uncertainty in scientific inferences
>•         In situ analysis for High Performance Computing
>•         Computation applied at the data sources
>•         Automated Machine Learning methods for identifying and
>extracting interesting features and patterns
>•         Methods for visualizing massive observational and model data
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>Target Audience 
>Data and computational science technologists, earth science research
>community, government program managers in data science and computation.
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>Papers 
>Extended abstracts are solicited that cover the research areas described
>in this workshop.  Speakers will be chosen from the abstracts.  Go here
><http://ieee-bigdata-earthscience.jpl.nasa.gov/papers> for
> information on abstract and paper submissions.  Papers are due August 1,
>2015.
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>Workshop Report 
>A workshop report will be produced highlighting the roadmap and
>technologies presented.
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>Program Committee 
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>Daniel Crichton, NASA Jet Propulsion Laboratory
>Yolanda Gil, University of Southern California, Information Sciences
>Institute
>Jacqueline Lamoigne-Stewart, NASA Goddard Space Flight Center
>Emily Law, NASA Jet Propulsion Laboratory
>Mike Little, NASA Headquarters
>Piyush Mehrotra , NASA Ames Research Center
>Jim Nelson, EROS Data Center/USGS
>Dean Williams, Lawrence Livermore National Laboratory
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