Data Mining Tools: From Web to Grid Architectures
Identifieur interne : 001616 ( Istex/Corpus ); précédent : 001615; suivant : 001617Data Mining Tools: From Web to Grid Architectures
Auteurs : Davide Anguita ; Arianna Poggi ; Fabio Rivieccio ; Marina ScapollaSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2005.
Abstract
Abstract: The paradigm of Grid computing is establishing as a novel, reliable and effective method to exploit a pool of hardware resources and make them available to the users. Data-mining benefits from the Grid as it often requires to run time consuming algorithms on large amounts of data which maybe reside on a different resource from the one having the proper data-mining algorithms. Also, in recent times, machine learning methods have been available to the purposes of knowledge discovery, which is a topic of interest for a large community of users. The present work is an account of the evolution of the ways in which a user can be provided with a data-mining service: from a web interface to a Grid service, the exploitation of a complex resource from a technical and a user-friendliness point of view is considered. More specifically, the goal is to show the interest/advantage of running data mining algorithm on the Grid. Such an environment can employ computational and storage resources in an efficient way, making it possible to open data mining services to Grid users and providing services to business contexts.
Url:
DOI: 10.1007/11508380_63
Links to Exploration step
ISTEX:5DBC6C531FF5C4ED72029DCB4437DD4B8B05E156Le document en format XML
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Data-mining benefits from the Grid as it often requires to run time consuming algorithms on large amounts of data which maybe reside on a different resource from the one having the proper data-mining algorithms. Also, in recent times, machine learning methods have been available to the purposes of knowledge discovery, which is a topic of interest for a large community of users. The present work is an account of the evolution of the ways in which a user can be provided with a data-mining service: from a web interface to a Grid service, the exploitation of a complex resource from a technical and a user-friendliness point of view is considered. More specifically, the goal is to show the interest/advantage of running data mining algorithm on the Grid. Such an environment can employ computational and storage resources in an efficient way, making it possible to open data mining services to Grid users and providing services to business contexts.</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>Davide Anguita</name><affiliations><json:string>DIBE – Dept. of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, 16145, Genova, Italy</json:string><json:string>E-mail: anguita@dibe.unige.it</json:string></affiliations></json:item><json:item><name>Arianna Poggi</name><affiliations><json:string>DIBE – Dept. of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, 16145, Genova, Italy</json:string><json:string>E-mail: apoggi@dibe.unige.it</json:string></affiliations></json:item><json:item><name>Fabio Rivieccio</name><affiliations><json:string>DIBE – Dept. of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, 16145, Genova, Italy</json:string><json:string>E-mail: rivieccio@dibe.unige.it</json:string></affiliations></json:item><json:item><name>Anna Marina Scapolla</name><affiliations><json:string>DIBE – Dept. of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, 16145, Genova, Italy</json:string><json:string>E-mail: ams@dibe.unige.it</json:string></affiliations></json:item></author><language><json:string>eng</json:string></language><abstract>Abstract: The paradigm of Grid computing is establishing as a novel, reliable and effective method to exploit a pool of hardware resources and make them available to the users. 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Data-mining benefits from the Grid as it often requires to run time consuming algorithms on large amounts of data which maybe reside on a different resource from the one having the proper data-mining algorithms. Also, in recent times, machine learning methods have been available to the purposes of knowledge discovery, which is a topic of interest for a large community of users. The present work is an account of the evolution of the ways in which a user can be provided with a data-mining service: from a web interface to a Grid service, the exploitation of a complex resource from a technical and a user-friendliness point of view is considered. More specifically, the goal is to show the interest/advantage of running data mining algorithm on the Grid. Such an environment can employ computational and storage resources in an efficient way, making it possible to open data mining services to Grid users and providing services to business contexts.</p></abstract><textClass><keywords scheme="Book Subject Collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass><keywords scheme="Book Subject Group"><list><label>I</label><label>I13022</label><label>I14029</label><label>I14010</label><label>I14045</label><label>I16021</label><label>I17028</label><item><term>Computer Science</term></item><item><term>Computer Communication Networks</term></item><item><term>Software Engineering</term></item><item><term>Programming Techniques</term></item><item><term>Operating Systems</term></item><item><term>Algorithm Analysis and Problem Complexity</term></item><item><term>Discrete Mathematics in Computer Science</term></item></list></keywords></textClass></profileDesc><revisionDesc><change 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ID="Aff9"><OrgName>Indian Institute of Technology</OrgName><OrgAddress><City>Madras</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff10"><OrgName>University of Dortmund</OrgName><OrgAddress><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff11"><OrgName>Massachusetts Institute of Technology</OrgName><OrgAddress><City>MA</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff12"><OrgName>New York University</OrgName><OrgAddress><City>NY</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff13"><OrgName>University of California</OrgName><OrgAddress><City>Berkeley</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff14"><OrgName>Rice University</OrgName><OrgAddress><City>Houston</City><State>TX</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff15"><OrgName>Max-Planck Institute of Computer Science</OrgName><OrgAddress><City>Saarbruecken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SeriesHeader><Book Language="En"><BookInfo BookProductType="Proceedings" ContainsESM="No" Language="En" MediaType="eBook" NumberingDepth="2" NumberingStyle="ContentOnly" OutputMedium="All" TocLevels="0"><BookID>978-3-540-32036-4</BookID><BookTitle>Advances in Grid Computing - EGC 2005</BookTitle><BookSubTitle>European Grid Conference, Amsterdam, The Netherlands, February 14-16, 2005, Revised Selected Papers</BookSubTitle><BookVolumeNumber>3470</BookVolumeNumber><BookSequenceNumber>3470</BookSequenceNumber><BookDOI>10.1007/b137919</BookDOI><BookTitleID>125002</BookTitleID><BookPrintISBN>978-3-540-26918-2</BookPrintISBN><BookElectronicISBN>978-3-540-32036-4</BookElectronicISBN><BookChapterCount>121</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag Berlin 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DisplayOrder="Western"><GivenName>Peter</GivenName><GivenName>M.</GivenName><GivenName>A.</GivenName><FamilyName>Sloot</FamilyName></EditorName><Contact><Email>sloot@science.uva.nl</Email></Contact></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Alfons</GivenName><GivenName>G.</GivenName><FamilyName>Hoekstra</FamilyName></EditorName><Contact><Email>alfons@science.uva.nl</Email></Contact></Editor><Editor AffiliationIDS="Aff18"><EditorName DisplayOrder="Western"><GivenName>Thierry</GivenName><FamilyName>Priol</FamilyName></EditorName><Contact><Email>thierry.priol@inria.fr</Email></Contact></Editor><Editor AffiliationIDS="Aff19"><EditorName DisplayOrder="Western"><GivenName>Alexander</GivenName><FamilyName>Reinefeld</FamilyName></EditorName><Contact><Email>Reinefeld@zib.de</Email></Contact></Editor><Editor AffiliationIDS="Aff20"><EditorName DisplayOrder="Western"><GivenName>Marian</GivenName><FamilyName>Bubak</FamilyName></EditorName><Contact><Email>bubak@science.uva.nl</Email></Contact></Editor><Affiliation ID="Aff16"><OrgDivision>Faculty of Sciences, Section of Computational Science</OrgDivision><OrgName>University of Amsterdam</OrgName><OrgAddress><Street>Kruislaan 403</Street><Postcode>1098</Postcode><City>Amsterdam</City><State>SJ</State><Country>The Netherlands</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgDivision>Section Computational Science</OrgDivision><OrgName>University of Amsterdam</OrgName><OrgAddress><Country>The Netherlands</Country></OrgAddress></Affiliation><Affiliation ID="Aff18"><OrgName>INRIA Rennes - Bretagne Atlantique</OrgName><OrgAddress><Street>Campus de Beaulieu</Street><Postcode>35042</Postcode><City>Rennes Cedex</City><Country>France</Country></OrgAddress></Affiliation><Affiliation ID="Aff19"><OrgName>Zuse Institute Berlin</OrgName><OrgAddress><Country> </Country></OrgAddress></Affiliation><Affiliation ID="Aff20"><OrgDivision>Institute of Computer Science</OrgDivision><OrgName>AGH</OrgName><OrgAddress><Country>Poland</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Chapter ID="Chap63" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>63</ChapterID><ChapterDOI>10.1007/11508380_63</ChapterDOI><ChapterSequenceNumber>63</ChapterSequenceNumber><ChapterTitle Language="En">Data Mining Tools: From Web to Grid Architectures</ChapterTitle><ChapterFirstPage>620</ChapterFirstPage><ChapterLastPage>629</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2005</CopyrightYear></ChapterCopyright><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>558</SeriesID><BookID>978-3-540-32036-4</BookID><BookTitle>Advances in Grid Computing - EGC 2005</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff21"><AuthorName DisplayOrder="Western"><GivenName>Davide</GivenName><FamilyName>Anguita</FamilyName></AuthorName><Contact><Email>anguita@dibe.unige.it</Email></Contact></Author><Author AffiliationIDS="Aff21"><AuthorName DisplayOrder="Western"><GivenName>Arianna</GivenName><FamilyName>Poggi</FamilyName></AuthorName><Contact><Email>apoggi@dibe.unige.it</Email></Contact></Author><Author AffiliationIDS="Aff21"><AuthorName DisplayOrder="Western"><GivenName>Fabio</GivenName><FamilyName>Rivieccio</FamilyName></AuthorName><Contact><Email>rivieccio@dibe.unige.it</Email></Contact></Author><Author AffiliationIDS="Aff21"><AuthorName DisplayOrder="Western"><GivenName>Anna</GivenName><GivenName>Marina</GivenName><FamilyName>Scapolla</FamilyName></AuthorName><Contact><Email>ams@dibe.unige.it</Email></Contact></Author><Affiliation ID="Aff21"><OrgDivision>DIBE – Dept. of Biophysical and Electronic Engineering</OrgDivision><OrgName>University of Genoa</OrgName><OrgAddress><Street>Via Opera Pia 11a</Street><Postcode>16145</Postcode><City>Genova</City><Country>Italy</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>The paradigm of Grid computing is establishing as a novel, reliable and effective method to exploit a pool of hardware resources and make them available to the users. Data-mining benefits from the Grid as it often requires to run time consuming algorithms on large amounts of data which maybe reside on a different resource from the one having the proper data-mining algorithms. Also, in recent times, machine learning methods have been available to the purposes of knowledge discovery, which is a topic of interest for a large community of users. The present work is an account of the evolution of the ways in which a user can be provided with a data-mining service: from a web interface to a Grid service, the exploitation of a complex resource from a technical and a user-friendliness point of view is considered. More specifically, the goal is to show the interest/advantage of running data mining algorithm on the Grid. Such an environment can employ computational and storage resources in an efficient way, making it possible to open data mining services to Grid users and providing services to business contexts.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Data Mining Tools: From Web to Grid Architectures</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Data Mining Tools: From Web to Grid Architectures</title></titleInfo><name type="personal"><namePart type="given">Davide</namePart><namePart type="family">Anguita</namePart><affiliation>DIBE – Dept. of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, 16145, Genova, Italy</affiliation><affiliation>E-mail: anguita@dibe.unige.it</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Arianna</namePart><namePart type="family">Poggi</namePart><affiliation>DIBE – Dept. of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, 16145, Genova, Italy</affiliation><affiliation>E-mail: apoggi@dibe.unige.it</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Fabio</namePart><namePart type="family">Rivieccio</namePart><affiliation>DIBE – Dept. of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, 16145, Genova, Italy</affiliation><affiliation>E-mail: rivieccio@dibe.unige.it</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Anna</namePart><namePart type="given">Marina</namePart><namePart type="family">Scapolla</namePart><affiliation>DIBE – Dept. of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, 16145, Genova, Italy</affiliation><affiliation>E-mail: ams@dibe.unige.it</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="research-article" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2005</dateIssued><copyrightDate encoding="w3cdtf">2005</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: The paradigm of Grid computing is establishing as a novel, reliable and effective method to exploit a pool of hardware resources and make them available to the users. Data-mining benefits from the Grid as it often requires to run time consuming algorithms on large amounts of data which maybe reside on a different resource from the one having the proper data-mining algorithms. Also, in recent times, machine learning methods have been available to the purposes of knowledge discovery, which is a topic of interest for a large community of users. The present work is an account of the evolution of the ways in which a user can be provided with a data-mining service: from a web interface to a Grid service, the exploitation of a complex resource from a technical and a user-friendliness point of view is considered. More specifically, the goal is to show the interest/advantage of running data mining algorithm on the Grid. 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