Provenance Collection Support in the Kepler Scientific Workflow System
Identifieur interne : 000281 ( Istex/Corpus ); précédent : 000280; suivant : 000282Provenance Collection Support in the Kepler Scientific Workflow System
Auteurs : Ilkay Altintas ; Oscar Barney ; Efrat Jaeger-FrankSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2006.
Abstract
Abstract: In many data-driven applications, analysis needs to be performed on scientific information obtained from several sources and generated by computations on distributed resources. Systematic analysis of this scientific information unleashes a growing need for automated data-driven applications that also can keep track of the provenance of the data and processes with little user interaction and overhead. Such data analysis can be facilitated by the recent advancements in scientific workflow systems. A major profit when using scientific workflow systems is the ability to make provenance collection a part of the workflow. Specifically, provenance should include not only the standard data lineage information but also information about the context in which the workflow was used, execution that processed the data, and the evolution of the workflow design. In this paper we describe a complete framework for data and process provenance in the Kepler Scientific Workflow System. We outline the requirements and issues related to data and workflow provenance in a multi-disciplinary workflow system and introduce how generic provenance capture can be facilitated in Kepler’s actor-oriented workflow environment. We also describe the usage of the stored provenance information for efficient rerun of scientific workflows.
Url:
DOI: 10.1007/11890850_14
Links to Exploration step
ISTEX:6035CD2171BD868852B7AAE419F394A2A6102527Le document en format XML
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Systematic analysis of this scientific information unleashes a growing need for automated data-driven applications that also can keep track of the provenance of the data and processes with little user interaction and overhead. Such data analysis can be facilitated by the recent advancements in scientific workflow systems. A major profit when using scientific workflow systems is the ability to make provenance collection a part of the workflow. Specifically, provenance should include not only the standard data lineage information but also information about the context in which the workflow was used, execution that processed the data, and the evolution of the workflow design. In this paper we describe a complete framework for data and process provenance in the Kepler Scientific Workflow System. We outline the requirements and issues related to data and workflow provenance in a multi-disciplinary workflow system and introduce how generic provenance capture can be facilitated in Kepler’s actor-oriented workflow environment. We also describe the usage of the stored provenance information for efficient rerun of scientific workflows.</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>I18032</label><label>I18040</label><label>I14045</label><label>I24040</label><label>I24067</label><label>I18059</label><item><term>Computer Science</term></item><item><term>Information Storage and Retrieval</term></item><item><term>Information Systems Applications (incl.Internet)</term></item><item><term>Operating Systems</term></item><item><term>Computers and Society</term></item><item><term>Management of Computing and Information Systems</term></item><item><term>Multimedia Information Systems</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="2006">Published</change><change xml:id="refBibs-istex" 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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>University of California</OrgName><OrgAddress><City>Los Angeles</City><State>CA</State><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>978-3-540-46303-0</BookID><BookTitle>Provenance and Annotation of Data</BookTitle><BookSubTitle>International Provenance and Annotation Workshop, IPAW 2006, Chicago, IL, USA, May 3-5, 2006, Revised Selected Papers</BookSubTitle><BookVolumeNumber>4145</BookVolumeNumber><BookSequenceNumber>4145</BookSequenceNumber><BookDOI>10.1007/11890850</BookDOI><BookTitleID>141855</BookTitleID><BookPrintISBN>978-3-540-46302-3</BookPrintISBN><BookElectronicISBN>978-3-540-46303-0</BookElectronicISBN><BookChapterCount>28</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2006</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I18032" Priority="1" Type="Secondary">Information Storage and Retrieval</BookSubject><BookSubject Code="I18040" Priority="2" Type="Secondary">Information Systems Applications (incl.Internet)</BookSubject><BookSubject Code="I14045" Priority="3" Type="Secondary">Operating Systems</BookSubject><BookSubject Code="I24040" Priority="4" Type="Secondary">Computers and Society</BookSubject><BookSubject Code="I24067" Priority="5" Type="Secondary">Management of Computing and Information Systems</BookSubject><BookSubject Code="I18059" Priority="6" Type="Secondary">Multimedia Information Systems</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Luc</GivenName><FamilyName>Moreau</FamilyName></EditorName><Contact><Email>L.Moreau@ecs.soton.ac.uk</Email></Contact></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Ian</GivenName><FamilyName>Foster</FamilyName></EditorName><Contact><Email>foster@mcs.anl.gov</Email></Contact></Editor><Affiliation ID="Aff16"><OrgDivision>School of Electronics and Computer Science</OrgDivision><OrgName>University of Southampton</OrgName><OrgAddress><Postcode>SO17 1BJ</Postcode><City>Southhampton</City><Country>UK</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgName>Mathematics & Computer Science Division, Argonne National Laboratory</OrgName><OrgAddress><Country> </Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part4"><PartInfo TocLevels="0"><PartID>4</PartID><PartSequenceNumber>4</PartSequenceNumber><PartTitle>Session 5: Workflow</PartTitle><PartChapterCount>4</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Provenance and Annotation of Data</BookTitle></PartContext></PartInfo><Chapter ID="Chap14" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>14</ChapterID><ChapterDOI>10.1007/11890850_14</ChapterDOI><ChapterSequenceNumber>14</ChapterSequenceNumber><ChapterTitle Language="En">Provenance Collection Support in the Kepler Scientific Workflow System</ChapterTitle><ChapterFirstPage>118</ChapterFirstPage><ChapterLastPage>132</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2006</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><PartID>4</PartID><BookID>978-3-540-46303-0</BookID><BookTitle>Provenance and Annotation of Data</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff18"><AuthorName DisplayOrder="Western"><GivenName>Ilkay</GivenName><FamilyName>Altintas</FamilyName></AuthorName><Contact><Email>altintas@sdsc.edu</Email></Contact></Author><Author AffiliationIDS="Aff19"><AuthorName DisplayOrder="Western"><GivenName>Oscar</GivenName><FamilyName>Barney</FamilyName></AuthorName><Contact><Email>oscar@sci.utah.edu</Email></Contact></Author><Author AffiliationIDS="Aff18"><AuthorName DisplayOrder="Western"><GivenName>Efrat</GivenName><FamilyName>Jaeger-Frank</FamilyName></AuthorName><Contact><Email>efrat@sdsc.edu</Email></Contact></Author><Affiliation ID="Aff18"><OrgDivision>San Diego Supercomputer Center</OrgDivision><OrgName>University of California</OrgName><OrgAddress><City>San Diego</City><Street>9500 Gilman Drive, San Diego</Street><State>CA</State><Postcode>92092-0505</Postcode><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff19"><OrgDivision>Scientific Computing and Imaging Institute</OrgDivision><OrgName>University of Utah</OrgName><OrgAddress><Street>50 S. Central Campus Drive</Street><City>Salt Lake City</City><State>UT</State><Postcode>84112</Postcode><Country>USA</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>In many data-driven applications, analysis needs to be performed on scientific information obtained from several sources and generated by computations on distributed resources. Systematic analysis of this scientific information unleashes a growing need for automated data-driven applications that also can keep track of the provenance of the data and processes with little user interaction and overhead. Such data analysis can be facilitated by the recent advancements in scientific workflow systems. A major profit when using scientific workflow systems is the ability to make provenance collection a part of the workflow. Specifically, provenance should include not only the standard data lineage information but also information about the context in which the workflow was used, execution that processed the data, and the evolution of the workflow design. In this paper we describe a complete framework for data and process provenance in the Kepler Scientific Workflow System. We outline the requirements and issues related to data and workflow provenance in a multi-disciplinary workflow system and introduce how generic provenance capture can be facilitated in Kepler’s actor-oriented workflow environment. We also describe the usage of the stored provenance information for efficient rerun of scientific workflows.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Provenance Collection Support in the Kepler Scientific Workflow System</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Provenance Collection Support in the Kepler Scientific Workflow System</title></titleInfo><name type="personal"><namePart type="given">Ilkay</namePart><namePart type="family">Altintas</namePart><affiliation>San Diego Supercomputer Center, University of California, 9500 Gilman Drive, San Diego, 92092-0505, San Diego, CA, USA</affiliation><affiliation>E-mail: altintas@sdsc.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Oscar</namePart><namePart type="family">Barney</namePart><affiliation>Scientific Computing and Imaging Institute, University of Utah, 50 S. Central Campus Drive, 84112, Salt Lake City, UT, USA</affiliation><affiliation>E-mail: oscar@sci.utah.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Efrat</namePart><namePart type="family">Jaeger-Frank</namePart><affiliation>San Diego Supercomputer Center, University of California, 9500 Gilman Drive, San Diego, 92092-0505, San Diego, CA, USA</affiliation><affiliation>E-mail: efrat@sdsc.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2006</dateIssued><copyrightDate encoding="w3cdtf">2006</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: In many data-driven applications, analysis needs to be performed on scientific information obtained from several sources and generated by computations on distributed resources. Systematic analysis of this scientific information unleashes a growing need for automated data-driven applications that also can keep track of the provenance of the data and processes with little user interaction and overhead. Such data analysis can be facilitated by the recent advancements in scientific workflow systems. A major profit when using scientific workflow systems is the ability to make provenance collection a part of the workflow. Specifically, provenance should include not only the standard data lineage information but also information about the context in which the workflow was used, execution that processed the data, and the evolution of the workflow design. In this paper we describe a complete framework for data and process provenance in the Kepler Scientific Workflow System. We outline the requirements and issues related to data and workflow provenance in a multi-disciplinary workflow system and introduce how generic provenance capture can be facilitated in Kepler’s actor-oriented workflow environment. We also describe the usage of the stored provenance information for efficient rerun of scientific workflows.</abstract><relatedItem type="host"><titleInfo><title>Provenance and Annotation of Data</title><subTitle>International Provenance and Annotation Workshop, IPAW 2006, Chicago, IL, USA, May 3-5, 2006, Revised Selected Papers</subTitle></titleInfo><name type="personal"><namePart type="given">Luc</namePart><namePart type="family">Moreau</namePart><affiliation>School of Electronics and Computer Science, University of Southampton, SO17 1BJ, Southhampton, UK</affiliation><affiliation>E-mail: L.Moreau@ecs.soton.ac.uk</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Ian</namePart><namePart type="family">Foster</namePart><affiliation>Mathematics & Computer Science Division, Argonne National Laboratory</affiliation><affiliation>E-mail: foster@mcs.anl.gov</affiliation><role><roleTerm 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