IN23C:
Geophysical Science Data Analytics Use Case Scenarios Posters


Session ID#: 8744

Session Description:
What's the big deal about data analytics?  What's big is the need to develop and implement technologies, services, applications, and techniques to efficiently analyze data and information for knowledge extraction.  Data analytics enables examination of large, distributed, heterogeneous data sets.  With the availability of, and need to quickly acquire, process, and analyze, varieties of needed available data, science research and prediction capabilities have evolved towards the use of innovative data analysis techniques, as well as computer technologies, such as machine learning applications, data mining, and visualization, etc. This session provides a forum for data scientists, science researchers, applications data users, educators, and decision makers to describe their data analytics usage scenarios that utilize techniques and technologies to overcome difficulties and unknowns associated with analyzing large amounts of heterogeneous data. In addition, insight can be gained by comparing the challenges and advantages of such capabilities and by sharing lessons learned.
Primary Convener:  Steven J Kempler, NASA Goddard Space Flight Center, GES DISC, Greenbelt, MD, United States
Conveners:  Robert R Downs, Columbia University of New York, Center for International Earth Science Information Network (CIESIN), Palisades, United States, Tiffany Joi Trapasso, NASA ASDC (SSAI Contractor), Hampton, VA, United States and John S Hughes, NASA Jet Propulsion Laboratory, Pasadena, CA, United States
Chairs:  Steven J Kempler, NASA Goddard Space Flight Center, GES DISC, Greenbelt, MD, United States and Tiffany Joi Trapasso, NASA ASDC (SSAI Contractor), Hampton, VA, United States
OSPA Liaison:  John S Hughes, NASA Jet Propulsion Laboratory, Pasadena, CA, United States

Cross-Listed:
  • A - Atmospheric Sciences
  • ED - Education
  • GC - Global Environmental Change
  • NH - Natural Hazards
Index Terms:

1914 Data mining [INFORMATICS]
1918 Decision analysis [INFORMATICS]
1942 Machine learning [INFORMATICS]
1968 Scientific reasoning/inference [INFORMATICS]

Abstracts Submitted to this Session:

An Analysis of Earth Science Data Analytics Use Cases (Invited) (60547)
Chung-Lin Shie, University of Maryland, Baltimore County (ret.), Baltimore, United States and Steven J Kempler, NASA Goddard Space Flight Center, GES DISC, Greenbelt, MD, United States
Using Predictive Analytics to Predict Power Outages from Severe Weather (60738)
David Walter Wanik1, Emmanouil N Anagnostou2, Brian Hartman3, Maria Eugenia Frediani2 and Marina Astitha4, (1)University of Connecticut, Operations and Information Management, Stamford, CT, United States, (2)University of Connecticut, Department of Civil and Environmental Engineering, Storrs Mansfield, CT, United States, (3)Brigham Young University, Provo, UT, United States, (4)University of Connecticut, Civil and Environmental Engineering, Groton, CT, United States
Shifting from Stewardship to Analytics of Massive Science Data (Invited) (66546)
Daniel J Crichton1, Richard J Doyle1, Emily Law2, Steve Hughes2, Thomas Huang2 and Ashish Mahabal3, (1)Jet Propulsion Laboratory, Pasadena, CA, United States, (2)NASA Jet Propulsion Laboratory, Pasadena, CA, United States, (3)California Institute of Technology, Pasadena, CA, United States
Lightweight Data Systems in the Cloud: Costs, Benefits and Best Practices (67521)
Rob Fatland, Microsoft Corporation, Redmond, WA, United States, Anthony A Arendt, University of Washington, eScience Institute, Seattle, United States, Bill Howe, University of Washington Seattle Campus, Seattle, WA, United States, Maggie Mae Bowman, Pacific Northwest National Laboratory, Richland, WA, United States and Joe Futrelle, Woods Hole Oceanographic Institution, Woods Hole, MA, United States
Deriving Earth Science Data Analytics Tools/Techniques Requirements (70123)
Steven J Kempler, NASA Goddard Space Flight Center, GES DISC, Greenbelt, MD, United States
Optimizing the decomposition of soil moisture time-series data using genetic algorithms (74811)
Chinmay Kulkarni1, Ole Jakob Mengshoel1, Aniruddha Basak1 and Kevin M Schmidt2, (1)Carnegie Mellon University Silicon Valley, Moffett Field, CA, United States, (2)USGS Western Regional Offices Menlo Park, Menlo Park, CA, United States
Big Data Analytics for Disaster Preparedness and Response of Mobile Communication Infrastructure during Natural Hazards (76415)
Lei Zhong1, Kiyoshi Takano2, Yusheng Ji3 and Shigeki Yamada3, (1)Earthquake Research Institute, University of Tokyo, Tokyo, Japan, (2)University of Tokyo, Bunkyo-ku, Japan, (3)National Institute of Informatics, Tokyo, Japan
Spatial-Temporal Data Mining Tools and Techniques for Improving Earth Science Metadata (80656)
Walter E Baskin1, Aaron Herbert2, Sophia Lin3 and John Kusterer1, (1)NASA Langley Research Center, Atmospheric Science Data Center, Hampton, United States, (2)Science Systems and Applications, Inc. Hampton, Hampton, VA, United States, (3)University of Illinois at Urbana Champaign, Computer Science, Urbana, IL, United States
Cloud-Based Computational Tools for Earth Science Applications (81199)
Anthony A Arendt, Geophysical Institute, Juneau, AK, United States, Rob Fatland, Microsoft Corporation, Redmond, WA, United States and Bill Howe, University of Washington Seattle Campus, Seattle, WA, United States
Characterization of Urban Landscape Using Super-Resolution UAS Data, Multiple Textural Scales and Data-Mining Techniques (82981)
Matthew Voss and Bruce Blundell, US Army Corps of Engineers Geospatial Research Laboratory, Alexandria, VA, United States

See more of: Earth and Space Science Informatics