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An Intelligent Archive Testbed Incorporating Data Mining

  • H.K. RamapriyanEmail author
  • D. Isaac
  • W. Yang
  • B. Bonnlander
  • D. Danks
Chapter
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

Many significant advances have occurred during the last two decades in remote sensing instrumentation, computation, storage, and communication technology. A series of Earth observing satellites have been launched by US and international agencies and have been operating and collecting global data on a regular basis. These advances have created a data rich environment for scientific research and applications. NASA’s Earth Observing System (EOS) Data and Information System (EOSDIS) has been operational since August 1994 with support for pre-EOS data. Currently, EOSDIS supports all the EOS missions including Terra (1999), Aqua (2002), ICESat (2002) and Aura (2004). EOSDIS has been effectively capturing, processing and archiving several terabytes of standard data products each day. It has also been distributing these data products at a rate of several terabytes per day to a diverse and globally distributed user community (Ramapriyan et al. 2009). There are other NASA-sponsored data system activities including measurement-based systems such as the Ocean Data Processing System and the Precipitation Processing system, and several projects under the Research, Education and Applications Solutions Network (REASoN), Making Earth Science Data Records for Use in Research Environments (MEaSUREs), and the Advancing Collaborative Connections for Earth-Sun System Science (ACCESS) programs. Together, these activities provide a rich set of resources constituting a “value chain” for users to obtain data at various levels ranging from raw radiances to interdisciplinary model outputs. The result has been a significant leap in our understanding of the Earth systems that all humans depend on for their enjoyment, livelihood, and survival.

Keywords

Vapor Pressure Deficit Land Cover Type Fire Occurrence Data Mining Algorithm Receive Operating Characteristic Curve Curve 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Acronyms

ACCESS

Advancing Collaborative Connections for Earth System Science

AUC

Area under the ROC curve

AVHRR

Advanced Very High-Resolution Radiometer

CRS

Coordinate Reference Systems

DAAC

Distributed Active Archive Center

DCE

Distributed Communication Environment

DDR

Double Data Rate

ECHO

EOS ClearingHOuse

EDC

EROS Data Center

EDS

Electronic Data Systems

EOS

Earth Observing System

EOSDIS

Earth Observing System Data and Information System

FPAR

Fractional Photosynthetically Active Radiation

FTP

File Transfer Protocol

GFLOPS

Giga (10**9) Floating Point Operations Per Second

GMU

George Mason University

GSFC

Goddard Space Flight Center

GSSD

Global Surface Summary of the Day

HDF

Hierarchical Data Format

IA

Intelligent Archive

IA-KBS

Intelligent Archive in the Context of a Knowledge Building System

IDU

Intelligent Data Understanding

IHMC

Institute for Human and Machine Cognition

LAI

Leaf Area Index

LP DAAC

Land Processes DAAC

LSDM

Large-Scale Data Mining

MCAT

Metadata Catalog

MDCE

MatLab Distributed Computing Engine

MEaSUREs

Making Earth Science Data Records for Use in Research Environ- ments

MODIS

Moderate-Resolution Imaging Spectroradiometer

NASA

National Aeronautics and Space Administration

NCDC

National Climate Data Center

NDVI

Normalized Difference Vegetation Index

NOAA

National Oceanic and Atmospheric Administration

PRECIP

precipitation

RAID

Redundant Array of Independent Disks

REASoN

Research, Education and Applications Solutions Network

RDS

Remote Data Storage

ROC

Receiver Operating Characteristic

SDRAM

Synchronous Dynamic Random Access Memory

SRB

Storage Resources Broker

TB

Terabyte

TCP/IP

Transmission Control Protocol/Internet Protocol

TMIN

Temperature, Minimum

TMAX

Temperature, Maximum

TOPS

Terrestrial Observation and Prediction System

US

United States

USFS

United States Forest Service

VPD

Vapor Pressure Deficit

Notes

Acknowledgment

The authors would like to thank the following individuals for their assistance and contributions of ideas to this work: G. McConaughy and C. Lynnes (NASA Goddard Space Flight Center-GSFC) – IA-KBS concepts, S. Morse (SOSACorp) – Intelligent Data Understanding (IDU) research assessment and testbed conceptual design, L. Di (GMU) – IA-KBS concepts, X. Li (GMU) – modeling and forecasting software execution on the testbed, T. Chu (IHMC) – assessment of software porting to testbed, and P. Smith (MacDonald, Dettwiler & Associates, Ltd.), B. Koenig (Electronic Data Systems Corporation - EDS) and C. Yee (Raytheon) – support for RDS/testbed interfaces. They would also like to thank C. Bock, D. Lowe and M. Esfandiari (Earth Science Data and Information System ESDIS Project, NASA GSFC) for their encouragement and support.

The data used in this study were obtained from several sources as indicated in Sect. 10.4.1. The sources include NOAA’s National Climate Data Center, US Forest Service, NASA’s Ames Research Center, and three of NASA’s EOSDIS Data Centers – Land Processes DAAC, National Snow and Ice Data Center and the Goddard Earth Science Data and Information Services Center.

References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • H.K. Ramapriyan
    • 1
    Email author
  • D. Isaac
    • 2
  • W. Yang
    • 3
  • B. Bonnlander
    • 4
  • D. Danks
    • 5
  1. 1.NASA Goddard Space Flight CenterGreenbeltUSA
  2. 2.BPS Consulting, Inc.BethesdaUSA
  3. 3.Center for Spatial Information Science and Systems (CSISS)George Mason UniversityGreenbeltUSA
  4. 4.Institute for Human and Machine CognitionPensacolaUSA
  5. 5.Carnegie Melon UniversityPittsburghUSA

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