Using spatial principles to optimize distributed computing for enabling the physical science discoveries.
Identifieur interne : 000034 ( PubMed/Checkpoint ); précédent : 000033; suivant : 000035Using spatial principles to optimize distributed computing for enabling the physical science discoveries.
Auteurs : Chaowei Yang [États-Unis] ; Huayi Wu ; Qunying Huang ; Zhenlong Li ; Jing LiSource :
- Proceedings of the National Academy of Sciences of the United States of America [ 1091-6490 ] ; 2011.
English descriptors
- KwdEn :
- MESH :
- methods : Informatics, Natural Science Disciplines.
- trends : Informatics.
- Climate Change, Computer Communication Networks, Computer Simulation, Computers, Demography, Weather.
Abstract
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 (i) enable data intensive science with efficient data/services search, access, and utilization, (ii) facilitate physical science studies with enabling high-performance computing capabilities, and (iii) 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.
DOI: 10.1073/pnas.0909315108
PubMed: 21444779
Affiliations:
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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 (i) enable data intensive science with efficient data/services search, access, and utilization, (ii) facilitate physical science studies with enabling high-performance computing capabilities, and (iii) 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.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">21444779</PMID><DateCreated><Year>2011</Year><Month>04</Month><Day>06</Day></DateCreated><DateCompleted><Year>2011</Year><Month>07</Month><Day>01</Day></DateCompleted><DateRevised><Year>2015</Year><Month>02</Month><Day>04</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1091-6490</ISSN><JournalIssue CitedMedium="Internet"><Volume>108</Volume><Issue>14</Issue><PubDate><Year>2011</Year><Month>Apr</Month><Day>5</Day></PubDate></JournalIssue><Title>Proceedings of the National Academy of Sciences of the United States of America</Title><ISOAbbreviation>Proc. Natl. Acad. Sci. U.S.A.</ISOAbbreviation></Journal><ArticleTitle>Using spatial principles to optimize distributed computing for enabling the physical science discoveries.</ArticleTitle><Pagination><MedlinePgn>5498-503</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1073/pnas.0909315108</ELocationID><Abstract><AbstractText>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 (i) enable data intensive science with efficient data/services search, access, and utilization, (ii) facilitate physical science studies with enabling high-performance computing capabilities, and (iii) 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.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Yang</LastName><ForeName>Chaowei</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>Joint Center for Intelligent Spatial Computing, College of Science, George Mason University, Fairfax, VA 22030-4444, USA. cyang3@gmu.edu</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wu</LastName><ForeName>Huayi</ForeName><Initials>H</Initials></Author><Author ValidYN="Y"><LastName>Huang</LastName><ForeName>Qunying</ForeName><Initials>Q</Initials></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Zhenlong</ForeName><Initials>Z</Initials></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Jing</ForeName><Initials>J</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013486">Research Support, U.S. Gov't, Non-P.H.S.</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2011</Year><Month>03</Month><Day>28</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Proc Natl Acad Sci U S A</MedlineTA><NlmUniqueID>7505876</NlmUniqueID><ISSNLinking>0027-8424</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>Proc Natl Acad Sci U S A. 2011 Apr 5;108(14):5492-7</RefSource><PMID Version="1">21444769</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Proc Natl Acad Sci U S A. 2011 Apr 5;108(14):5488-91</RefSource><PMID Version="1">21467227</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Proc Natl Acad Sci U S A. 2011 Apr 5;108(14):5510-5</RefSource><PMID Version="1">21444825</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Proc Natl Acad Sci U S A. 2011 Apr 5;108(14):5504-9</RefSource><PMID Version="1">21444819</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="N" UI="D057231">Climate Change</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D003195">Computer Communication Networks</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D003198">Computer Simulation</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D003201">Computers</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D003710">Demography</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D048088">Informatics</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000379">methods</QualifierName><QualifierName MajorTopicYN="N" UI="Q000639">trends</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D010811">Natural Science Disciplines</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D014887">Weather</DescriptorName></MeshHeading></MeshHeadingList><OtherID Source="NLM">PMC3078382</OtherID></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="aheadofprint"><Year>2011</Year><Month>3</Month><Day>28</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2011</Year><Month>3</Month><Day>30</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2011</Year><Month>3</Month><Day>30</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2011</Year><Month>7</Month><Day>2</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pii">0909315108</ArticleId><ArticleId IdType="doi">10.1073/pnas.0909315108</ArticleId><ArticleId IdType="pubmed">21444779</ArticleId><ArticleId IdType="pmc">PMC3078382</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country><region><li>Virginie</li></region></list><tree><noCountry><name sortKey="Huang, Qunying" sort="Huang, Qunying" uniqKey="Huang Q" first="Qunying" last="Huang">Qunying Huang</name><name sortKey="Li, Jing" sort="Li, Jing" uniqKey="Li J" first="Jing" last="Li">Jing Li</name><name sortKey="Li, Zhenlong" sort="Li, Zhenlong" uniqKey="Li Z" first="Zhenlong" last="Li">Zhenlong Li</name><name sortKey="Wu, Huayi" sort="Wu, Huayi" uniqKey="Wu H" first="Huayi" last="Wu">Huayi Wu</name></noCountry><country name="États-Unis"><region name="Virginie"><name sortKey="Yang, Chaowei" sort="Yang, Chaowei" uniqKey="Yang C" first="Chaowei" last="Yang">Chaowei Yang</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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