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<div>FYI…</div>
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<div>Best regards,</div>
<div><span class="Apple-tab-span" style="white-space:pre"></span>Dean</div>
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Workshop on Data and Computational Science Technologies for Earth Science Research</h1>
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<b style="margin: 0px; padding: 0px; "><a class="external-link" href="http://cci.drexel.edu/bigdata/bigdata2015/" target="_self" title="2015 IEEE Big Data Conference" style="margin: 0px; padding: 0px; outline: none; border-bottom-style: solid; border-bottom-width: 0.1em; border-bottom-color: rgb(204, 204, 204); text-decoration: none; color: rgb(66, 117, 151); ">2015 IEEE
Big Data Conference</a></b></h3>
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<b style="margin: 0px; padding: 0px; ">October 29, 2015 – November 1, 2015</b></h3>
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<b style="margin: 0px; padding: 0px; ">Santa Clara, CA</b></h3>
<div style="text-align: center;"><b style="margin: 0px; padding: 0px; "><a href="http://ieee-bigdata-earthscience.jpl.nasa.gov">http://ieee-bigdata-earthscience.jpl.nasa.gov</a></b></div>
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<b style="margin: 0px; padding: 0px; ">Workshop Description</b></h3>
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<p class=" " style="margin: 0px 0px 1em; padding: 0px; ">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.</p>
<p class=" " style="margin: 0px 0px 1em; padding: 0px; ">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.</p>
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<b style="margin: 0px; padding: 0px; ">Technical Focus</b></h3>
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<p style="margin: 0px 0px 1em; padding: 0px; ">I. Architectural considerations/tradeoffs for integrating the entire data lifecycle from observational systems to climate modeling and research</p>
<p style="margin: 0px 0px 1em; padding: 0px; ">• Approaches for scaling observing systems for satellite, airborne and ground-based sensors</p>
<p style="margin: 0px 0px 1em; padding: 0px; ">• Integration of computational methods with observing systems</p>
<p style="margin: 0px 0px 1em; padding: 0px; ">• New concepts for data intensive missions</p>
<p style="margin: 0px 0px 1em; padding: 0px; ">• Integration of data, computing/HPC/cloud, and algorithms</p>
<p style="margin: 0px 0px 1em; padding: 0px; ">• Cloud computing, software as a service</p>
<p style="margin: 0px 0px 1em; padding: 0px; ">• New technologies (e.g., distributed frameworks, database technologies, search, etc) for scaling the architecture</p>
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<p style="margin: 0px 0px 1em; padding: 0px; ">II. Onboard/Sensor-based Computing</p>
<p style="margin: 0px 0px 1em; padding: 0px; ">• Embedded and real-time data reduction and triage/analytics methods</p>
<p style="margin: 0px 0px 1em; padding: 0px; ">• Managing bandwidth constraints for high volume instruments</p>
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<p style="margin: 0px 0px 1em; padding: 0px; ">III. Scalable Data Management and Computation for ground-based systems</p>
<p style="margin: 0px 0px 1em; padding: 0px; ">• Capturing well-architected and curated data repositories</p>
<p style="margin: 0px 0px 1em; padding: 0px; ">• Data and semantic information architectures</p>
<p style="margin: 0px 0px 1em; padding: 0px; ">• Architecting automated pipelines for data capture</p>
<p style="margin: 0px 0px 1em; padding: 0px; ">• Enabling analytics on data pipelines for computation, data discovery, event detection, reduction, etc</p>
<p style="margin: 0px 0px 1em; padding: 0px; ">• Open source data science frameworks </p>
<p style="margin: 0px 0px 1em; padding: 0px; ">• Cloud computing</p>
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<p style="margin: 0px 0px 1em; padding: 0px; ">IV. Scalable Data Analytics for Massively Distributed Data</p>
<p style="margin: 0px 0px 1em; padding: 0px; ">• Access and integration of highly distributed, heterogeneous data</p>
<p style="margin: 0px 0px 1em; padding: 0px; ">• Novel statistical approaches for data integration and fusion</p>
<p style="margin: 0px 0px 1em; padding: 0px; ">• Sampling strategies from massive data repositories</p>
<p style="margin: 0px 0px 1em; padding: 0px; ">• Uncertainty in scientific inferences</p>
<p style="margin: 0px 0px 1em; padding: 0px; ">• In situ analysis for High Performance Computing</p>
<p style="margin: 0px 0px 1em; padding: 0px; ">• Computation applied at the data sources</p>
<p style="margin: 0px 0px 1em; padding: 0px; ">• Automated Machine Learning methods for identifying and extracting interesting features and patterns</p>
<p style="margin: 0px 0px 1em; padding: 0px; ">• Methods for visualizing massive observational and model data</p>
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<b style="margin: 0px; padding: 0px; ">Target Audience</b></h3>
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<p style="margin: 0px 0px 1em; padding: 0px; ">Data and computational science technologists, earth science research community, government program managers in data science and computation.</p>
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<b style="margin: 0px; padding: 0px; ">Papers</b></h3>
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<p style="margin: 0px 0px 1em; padding: 0px; ">Extended abstracts are solicited that cover the research areas described in this workshop. Speakers will be chosen from the abstracts. Go <a title="" href="http://ieee-bigdata-earthscience.jpl.nasa.gov/papers" class="internal-link" target="_self" style="margin: 0px; padding: 0px; outline: none; text-decoration: none; color: rgb(66, 117, 151); border-bottom-width: 0.1em; border-bottom-style: solid; border-bottom-color: rgb(204, 204, 204); ">here</a> for
information on abstract and paper submissions. Papers are due August 1, 2015.</p>
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<b style="margin: 0px; padding: 0px; ">Workshop Report</b></h3>
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<p style="margin: 0px 0px 1em; padding: 0px; ">A workshop report will be produced highlighting the roadmap and technologies presented.</p>
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Program Committee</h3>
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<p>Daniel Crichton, NASA Jet Propulsion Laboratory</p>
<p>Yolanda Gil, University of Southern California, Information Sciences Institute</p>
<p>Jacqueline Lamoigne-Stewart, NASA Goddard Space Flight Center</p>
<p>Emily Law, NASA Jet Propulsion Laboratory</p>
<p>Mike Little, NASA Headquarters</p>
<p>Piyush Mehrotra , NASA Ames Research Center</p>
<p>Jim Nelson, EROS Data Center/USGS</p>
<p>Dean Williams, Lawrence Livermore National Laboratory</p>
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