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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Using spatial principles to optimize distributed computing for enabling the physical science discoveries</title>
<author><name sortKey="Yang, Chaowei" sort="Yang, Chaowei" uniqKey="Yang C" first="Chaowei" last="Yang">Chaowei Yang</name>
<affiliation><nlm:aff id="aff1">Joint Center for Intelligent Spatial Computing, College of Science, George Mason University, Fairfax, VA 22030-4444</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Wu, Huayi" sort="Wu, Huayi" uniqKey="Wu H" first="Huayi" last="Wu">Huayi Wu</name>
<affiliation><nlm:aff id="aff1">Joint Center for Intelligent Spatial Computing, College of Science, George Mason University, Fairfax, VA 22030-4444</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Huang, Qunying" sort="Huang, Qunying" uniqKey="Huang Q" first="Qunying" last="Huang">Qunying Huang</name>
<affiliation><nlm:aff id="aff1">Joint Center for Intelligent Spatial Computing, College of Science, George Mason University, Fairfax, VA 22030-4444</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Li, Zhenlong" sort="Li, Zhenlong" uniqKey="Li Z" first="Zhenlong" last="Li">Zhenlong Li</name>
<affiliation><nlm:aff id="aff1">Joint Center for Intelligent Spatial Computing, College of Science, George Mason University, Fairfax, VA 22030-4444</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Li, Jing" sort="Li, Jing" uniqKey="Li J" first="Jing" last="Li">Jing Li</name>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">PMC</idno>
<idno type="pmid">21444779</idno>
<idno type="pmc">3078382</idno>
<idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3078382</idno>
<idno type="RBID">PMC:3078382</idno>
<idno type="doi">10.1073/pnas.0909315108</idno>
<date when="2011">2011</date>
<idno type="wicri:Area/Pmc/Corpus">000393</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Using spatial principles to optimize distributed computing for enabling the physical science discoveries</title>
<author><name sortKey="Yang, Chaowei" sort="Yang, Chaowei" uniqKey="Yang C" first="Chaowei" last="Yang">Chaowei Yang</name>
<affiliation><nlm:aff id="aff1">Joint Center for Intelligent Spatial Computing, College of Science, George Mason University, Fairfax, VA 22030-4444</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Wu, Huayi" sort="Wu, Huayi" uniqKey="Wu H" first="Huayi" last="Wu">Huayi Wu</name>
<affiliation><nlm:aff id="aff1">Joint Center for Intelligent Spatial Computing, College of Science, George Mason University, Fairfax, VA 22030-4444</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Huang, Qunying" sort="Huang, Qunying" uniqKey="Huang Q" first="Qunying" last="Huang">Qunying Huang</name>
<affiliation><nlm:aff id="aff1">Joint Center for Intelligent Spatial Computing, College of Science, George Mason University, Fairfax, VA 22030-4444</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Li, Zhenlong" sort="Li, Zhenlong" uniqKey="Li Z" first="Zhenlong" last="Li">Zhenlong Li</name>
<affiliation><nlm:aff id="aff1">Joint Center for Intelligent Spatial Computing, College of Science, George Mason University, Fairfax, VA 22030-4444</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Li, Jing" sort="Li, Jing" uniqKey="Li J" first="Jing" last="Li">Jing Li</name>
</author>
</analytic>
<series><title level="j">Proceedings of the National Academy of Sciences of the United States of America</title>
<idno type="ISSN">0027-8424</idno>
<idno type="eISSN">1091-6490</idno>
<imprint><date when="2011">2011</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc><textClass></textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en"><p>Contemporary physical science studies rely on the effective analyses of geographically dispersed spatial data and simulations of physical phenomena. Single computers and generic high-end computing are not sufficient to process the data for complex physical science analysis and simulations, which can be successfully supported only through distributed computing, best optimized through the application of spatial principles. Spatial computing, the computing aspect of a spatial cyberinfrastructure, refers to a computing paradigm that utilizes spatial principles to optimize distributed computers to catalyze advancements in the physical sciences. Spatial principles govern the interactions between scientific parameters across space and time by providing the spatial connections and constraints to drive the progression of the phenomena. Therefore, spatial computing studies could better position us to leverage spatial principles in simulating physical phenomena and, by extension, advance the physical sciences. Using geospatial science as an example, this paper illustrates through three research examples how spatial computing could (<italic>i</italic>
) enable data intensive science with efficient data/services search, access, and utilization, (<italic>ii</italic>
) facilitate physical science studies with enabling high-performance computing capabilities, and (<italic>iii</italic>
) empower scientists with multidimensional visualization tools to understand observations and simulations. The research examples demonstrate that spatial computing is of critical importance to design computing methods to catalyze physical science studies with better data access, phenomena simulation, and analytical visualization. We envision that spatial computing will become a core technology that drives fundamental physical science advancements in the 21st century.</p>
</div>
</front>
</TEI>
<pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Proc Natl Acad Sci U S A</journal-id>
<journal-id journal-id-type="hwp">pnas</journal-id>
<journal-id journal-id-type="pmc">pnas</journal-id>
<journal-id journal-id-type="publisher-id">PNAS</journal-id>
<journal-title-group><journal-title>Proceedings of the National Academy of Sciences of the United States of America</journal-title>
</journal-title-group>
<issn pub-type="ppub">0027-8424</issn>
<issn pub-type="epub">1091-6490</issn>
<publisher><publisher-name>National Academy of Sciences</publisher-name>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">21444779</article-id>
<article-id pub-id-type="pmc">3078382</article-id>
<article-id pub-id-type="publisher-id">200909315</article-id>
<article-id pub-id-type="doi">10.1073/pnas.0909315108</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Spatial Cyberinfrastructure Special Feature</subject>
<subj-group><subject>Research Articles</subject>
</subj-group>
</subj-group>
<subj-group subj-group-type="heading"><subject>Physical Sciences</subject>
<subj-group><subject>Computer Sciences</subject>
</subj-group>
</subj-group>
<series-title>Spatial Cyberinfrastructure Special Feature</series-title>
</article-categories>
<title-group><article-title>Using spatial principles to optimize distributed computing for enabling the physical science discoveries</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Yang</surname>
<given-names>Chaowei</given-names>
</name>
<xref ref-type="corresp" rid="cor1"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff1"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>Wu</surname>
<given-names>Huayi</given-names>
</name>
<xref ref-type="aff" rid="aff1"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>Huang</surname>
<given-names>Qunying</given-names>
</name>
<xref ref-type="aff" rid="aff1"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>Li</surname>
<given-names>Zhenlong</given-names>
</name>
<xref ref-type="aff" rid="aff1"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>Li</surname>
<given-names>Jing</given-names>
</name>
</contrib>
<xref ref-type="aff" rid="aff1"></xref>
<aff id="aff1">Joint Center for Intelligent Spatial Computing, College of Science, George Mason University, Fairfax, VA 22030-4444</aff>
</contrib-group>
<author-notes><corresp id="cor1"><sup>1</sup>
To whom correspondence should be addressed. E-mail: <email>cyang3@gmu.edu</email>
.</corresp>
<fn fn-type="edited-by"><p>Edited by Michael Goodchild, University of California, Santa Barbara, CA, and approved December 14, 2010 (received for review August 15, 2009)</p>
</fn>
<fn fn-type="participating-researchers"><p>Author contributions: C.Y. designed research; H.W. managed research; and C.Y., H.W., Q.H., Z.L., and J.L. performed research, analyzed data, and wrote the paper.</p>
</fn>
</author-notes>
<pub-date pub-type="ppub"><day>5</day>
<month>4</month>
<year>2011</year>
</pub-date>
<pub-date pub-type="epub"><day>28</day>
<month>3</month>
<year>2011</year>
</pub-date>
<volume>108</volume>
<issue>14</issue>
<fpage>5498</fpage>
<lpage>5503</lpage>
<self-uri xlink:title="pdf" xlink:type="simple" xlink:href="pnas.0909315108.pdf"></self-uri>
<abstract><p>Contemporary physical science studies rely on the effective analyses of geographically dispersed spatial data and simulations of physical phenomena. Single computers and generic high-end computing are not sufficient to process the data for complex physical science analysis and simulations, which can be successfully supported only through distributed computing, best optimized through the application of spatial principles. Spatial computing, the computing aspect of a spatial cyberinfrastructure, refers to a computing paradigm that utilizes spatial principles to optimize distributed computers to catalyze advancements in the physical sciences. Spatial principles govern the interactions between scientific parameters across space and time by providing the spatial connections and constraints to drive the progression of the phenomena. Therefore, spatial computing studies could better position us to leverage spatial principles in simulating physical phenomena and, by extension, advance the physical sciences. Using geospatial science as an example, this paper illustrates through three research examples how spatial computing could (<italic>i</italic>
) enable data intensive science with efficient data/services search, access, and utilization, (<italic>ii</italic>
) facilitate physical science studies with enabling high-performance computing capabilities, and (<italic>iii</italic>
) empower scientists with multidimensional visualization tools to understand observations and simulations. The research examples demonstrate that spatial computing is of critical importance to design computing methods to catalyze physical science studies with better data access, phenomena simulation, and analytical visualization. We envision that spatial computing will become a core technology that drives fundamental physical science advancements in the 21st century.</p>
</abstract>
<kwd-group><kwd>cyberinfrastructure</kwd>
<kwd>spatial data infrastructure</kwd>
<kwd>spatial thinking</kwd>
<kwd>spatiotemporal</kwd>
<kwd>cloud computing</kwd>
</kwd-group>
</article-meta>
</front>
</pmc>
</record>
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