Enabling Big Geoscience Data Analytics with a Cloud-Based, MapReduce-Enabled and Service-Oriented Workflow Framework
Identifieur interne : 000069 ( Pmc/Curation ); précédent : 000068; suivant : 000070Enabling Big Geoscience Data Analytics with a Cloud-Based, MapReduce-Enabled and Service-Oriented Workflow Framework
Auteurs : Zhenlong Li [États-Unis] ; Chaowei Yang [États-Unis] ; Baoxuan Jin [États-Unis, République populaire de Chine] ; Manzhu Yu [États-Unis] ; Kai Liu [États-Unis] ; Min Sun [États-Unis] ; Matthew Zhan [États-Unis]Source :
- PLoS ONE [ 1932-6203 ] ; 2015.
Abstract
Geoscience observations and model simulations are generating vast amounts of multi-dimensional data. Effectively analyzing these data are essential for geoscience studies. However, the tasks are challenging for geoscientists because processing the massive amount of data is both computing and data intensive in that data analytics requires complex procedures and multiple tools. To tackle these challenges, a scientific workflow framework is proposed for big geoscience data analytics. In this framework techniques are proposed by leveraging cloud computing, MapReduce, and Service Oriented Architecture (SOA). Specifically, HBase is adopted for storing and managing big geoscience data across distributed computers. MapReduce-based algorithm framework is developed to support parallel processing of geoscience data. And service-oriented workflow architecture is built for supporting on-demand complex data analytics in the cloud environment. A proof-of-concept prototype tests the performance of the framework. Results show that this innovative framework significantly improves the efficiency of big geoscience data analytics by reducing the data processing time as well as simplifying data analytical procedures for geoscientists.
Url:
DOI: 10.1371/journal.pone.0116781
PubMed: 25742012
PubMed Central: 4351198
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<front><div type="abstract" xml:lang="en"><p>Geoscience observations and model simulations are generating vast amounts of multi-dimensional data. Effectively analyzing these data are essential for geoscience studies. However, the tasks are challenging for geoscientists because processing the massive amount of data is both computing and data intensive in that data analytics requires complex procedures and multiple tools. To tackle these challenges, a scientific workflow framework is proposed for big geoscience data analytics. In this framework techniques are proposed by leveraging cloud computing, MapReduce, and Service Oriented Architecture (SOA). Specifically, HBase is adopted for storing and managing big geoscience data across distributed computers. MapReduce-based algorithm framework is developed to support parallel processing of geoscience data. And service-oriented workflow architecture is built for supporting on-demand complex data analytics in the cloud environment. A proof-of-concept prototype tests the performance of the framework. Results show that this innovative framework significantly improves the efficiency of big geoscience data analytics by reducing the data processing time as well as simplifying data analytical procedures for geoscientists.</p>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS One</journal-id>
<journal-id journal-id-type="iso-abbrev">PLoS ONE</journal-id>
<journal-id journal-id-type="publisher-id">plos</journal-id>
<journal-id journal-id-type="pmc">plosone</journal-id>
<journal-title-group><journal-title>PLoS ONE</journal-title>
</journal-title-group>
<issn pub-type="epub">1932-6203</issn>
<publisher><publisher-name>Public Library of Science</publisher-name>
<publisher-loc>San Francisco, CA USA</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">25742012</article-id>
<article-id pub-id-type="pmc">4351198</article-id>
<article-id pub-id-type="doi">10.1371/journal.pone.0116781</article-id>
<article-id pub-id-type="publisher-id">PONE-D-14-42409</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>Enabling Big Geoscience Data Analytics with a Cloud-Based, MapReduce-Enabled and Service-Oriented Workflow Framework</article-title>
<alt-title alt-title-type="running-head">Developing a New Framework to Enable Big Geoscience Data Analytics</alt-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Li</surname>
<given-names>Zhenlong</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Yang</surname>
<given-names>Chaowei</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
<xref rid="cor001" ref-type="corresp">*</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Jin</surname>
<given-names>Baoxuan</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff002"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Yu</surname>
<given-names>Manzhu</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Liu</surname>
<given-names>Kai</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Sun</surname>
<given-names>Min</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Zhan</surname>
<given-names>Matthew</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff003"><sup>3</sup>
</xref>
</contrib>
</contrib-group>
<aff id="aff001"><label>1</label>
<addr-line>NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, United States of America</addr-line>
</aff>
<aff id="aff002"><label>2</label>
<addr-line>Yunnan Provincial Geomatics Center, Yunnan Bureau of Surveying, Mapping, and GeoInformation, Kunming,Yunnan, China</addr-line>
</aff>
<aff id="aff003"><label>3</label>
<addr-line>Department of Computer Science, University of Texas—Austin, Austin, Texas, United States of America</addr-line>
</aff>
<contrib-group><contrib contrib-type="editor"><name><surname>Gomez-Gesteira</surname>
<given-names>Moncho</given-names>
</name>
<role>Academic Editor</role>
<xref ref-type="aff" rid="edit1"></xref>
</contrib>
</contrib-group>
<aff id="edit1"><addr-line>University of Vigo, SPAIN</addr-line>
</aff>
<author-notes><fn fn-type="conflict" id="coi001"><p><bold>Competing Interests: </bold>
The authors have declared that no competing interests exist.</p>
</fn>
<fn fn-type="con" id="contrib001"><p>Conceived and designed the experiments: CY ZL BJ. Performed the experiments: ZL KL MY MZ. Analyzed the data: ZL CY BJ KL MY. Contributed reagents/materials/analysis tools: ZL CY BJ MS KL. Wrote the paper: ZL CY MY BJ.</p>
</fn>
<corresp id="cor001">* E-mail: <email>cyang3@gmu.edu</email>
</corresp>
</author-notes>
<pub-date pub-type="epub"><day>5</day>
<month>3</month>
<year>2015</year>
</pub-date>
<pub-date pub-type="collection"><year>2015</year>
</pub-date>
<volume>10</volume>
<issue>3</issue>
<elocation-id>e0116781</elocation-id>
<history><date date-type="received"><day>20</day>
<month>9</month>
<year>2014</year>
</date>
<date date-type="accepted"><day>14</day>
<month>12</month>
<year>2014</year>
</date>
</history>
<permissions><copyright-year>2015</copyright-year>
<copyright-holder>Li et al</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</ext-link>
, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited</license-p>
</license>
</permissions>
<self-uri content-type="pdf" xlink:type="simple" xlink:href="pone.0116781.pdf"></self-uri>
<abstract><p>Geoscience observations and model simulations are generating vast amounts of multi-dimensional data. Effectively analyzing these data are essential for geoscience studies. However, the tasks are challenging for geoscientists because processing the massive amount of data is both computing and data intensive in that data analytics requires complex procedures and multiple tools. To tackle these challenges, a scientific workflow framework is proposed for big geoscience data analytics. In this framework techniques are proposed by leveraging cloud computing, MapReduce, and Service Oriented Architecture (SOA). Specifically, HBase is adopted for storing and managing big geoscience data across distributed computers. MapReduce-based algorithm framework is developed to support parallel processing of geoscience data. And service-oriented workflow architecture is built for supporting on-demand complex data analytics in the cloud environment. A proof-of-concept prototype tests the performance of the framework. Results show that this innovative framework significantly improves the efficiency of big geoscience data analytics by reducing the data processing time as well as simplifying data analytical procedures for geoscientists.</p>
</abstract>
<funding-group><funding-statement>This research is supported by NSF (PLR-1349259, IIP-1338925, CNS-1117300) and NASA (NNG12PP37I). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</funding-statement>
</funding-group>
<counts><fig-count count="13"></fig-count>
<table-count count="2"></table-count>
<page-count count="23"></page-count>
</counts>
<custom-meta-group><custom-meta id="data-availability"><meta-name>Data Availability</meta-name>
<meta-value>All relevant data are within the paper.</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
<notes><title>Data Availability</title>
<p>All relevant data are within the paper.</p>
</notes>
</front>
</pmc>
</record>
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