Towards Case-Based Support for e-Science Workflow Generation by Mining Provenance
Identifieur interne : 000251 ( Istex/Corpus ); précédent : 000250; suivant : 000252Towards Case-Based Support for e-Science Workflow Generation by Mining Provenance
Auteurs : David Leake ; Joseph Kendall-MorwickSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2008.
Abstract
Abstract: e-Science brings large-scale computation to bear on scientific problems, often by performing sequences of computational tasks organized into workflows and executed on distributed Web resources. Sophisticated AI tools have been developed to apply knowledge-rich methods to compose scientific workflows by generative planning, but the required knowledge can be difficult to acquire. Current work by the cyberinfrastructure community aims to routinely capture provenance during workflow execution, which would provide a new experience-based knowledge source for workflow generation: large-scale databases of workflow execution traces. This paper proposes exploiting these databases with a “knowledge light” approach to reuse, applying CBR methods to those traces to support scientists’ workflow generation process. This paper introduces e-Science workflows as a CBR domain, sketches key technical issues, and illustrates directions towards addressing these issues through ongoing research on Phala, a system which supports workflow generation by aiding re-use of portions of prior workflows. The paper uses workflow data collected by the myGrid and myExperiment projects in experiments which suggest that Phala’s methods have promise for assisting workflow composition in the context of scientific experimentation.
Url:
DOI: 10.1007/978-3-540-85502-6_18
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ISTEX:A111555C2F0851AAC72ED7E5F7BF7153674619A8Le document en format XML
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Sophisticated AI tools have been developed to apply knowledge-rich methods to compose scientific workflows by generative planning, but the required knowledge can be difficult to acquire. Current work by the cyberinfrastructure community aims to routinely capture provenance during workflow execution, which would provide a new experience-based knowledge source for workflow generation: large-scale databases of workflow execution traces. This paper proposes exploiting these databases with a “knowledge light” approach to reuse, applying CBR methods to those traces to support scientists’ workflow generation process. This paper introduces e-Science workflows as a CBR domain, sketches key technical issues, and illustrates directions towards addressing these issues through ongoing research on Phala, a system which supports workflow generation by aiding re-use of portions of prior workflows. 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Sophisticated AI tools have been developed to apply knowledge-rich methods to compose scientific workflows by generative planning, but the required knowledge can be difficult to acquire. Current work by the cyberinfrastructure community aims to routinely capture provenance during workflow execution, which would provide a new experience-based knowledge source for workflow generation: large-scale databases of workflow execution traces. This paper proposes exploiting these databases with a “knowledge light” approach to reuse, applying CBR methods to those traces to support scientists’ workflow generation process. This paper introduces e-Science workflows as a CBR domain, sketches key technical issues, and illustrates directions towards addressing these issues through ongoing research on Phala, a system which supports workflow generation by aiding re-use of portions of prior workflows. 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Woodlawn Avenue</Street><City>Bloomington</City><Postcode>IN 47405</Postcode><Country>U.S.A.</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>e-Science brings large-scale computation to bear on scientific problems, often by performing sequences of computational tasks organized into workflows and executed on distributed Web resources. Sophisticated AI tools have been developed to apply knowledge-rich methods to compose scientific workflows by generative planning, but the required knowledge can be difficult to acquire. Current work by the cyberinfrastructure community aims to routinely capture provenance during workflow execution, which would provide a new experience-based knowledge source for workflow generation: large-scale databases of workflow execution traces. This paper proposes exploiting these databases with a “knowledge light” approach to reuse, applying CBR methods to those traces to support scientists’ workflow generation process. This paper introduces e-Science workflows as a CBR domain, sketches key technical issues, and illustrates directions towards addressing these issues through ongoing research on Phala, a system which supports workflow generation by aiding re-use of portions of prior workflows. The paper uses workflow data collected by the myGrid and myExperiment projects in experiments which suggest that Phala’s methods have promise for assisting workflow composition in the context of scientific experimentation.</Para></Abstract><ArticleNote Type="Misc"><SimplePara>This material is based on work supported by the National Science Foundation under Grant No. OCI-0721674. Our thanks to Beth Plale, Yogesh Simmhan, and the rest of the Indiana University SDCI group at IU for their vital contributions to this work, and to Yogesh Simmhan and the anonymous reviewers for valuable comments on a draft of this paper.</SimplePara></ArticleNote></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Towards Case-Based Support for e-Science Workflow Generation by Mining Provenance</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Towards Case-Based Support for e-Science Workflow Generation by Mining Provenance</title></titleInfo><name type="personal"><namePart type="given">David</namePart><namePart type="family">Leake</namePart><affiliation>Computer Science Department, Indiana University, Lindley Hall 215 150 S. Woodlawn Avenue, IN 47405, Bloomington, U.S.A.</affiliation><affiliation>E-mail: leake@cs.indiana.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Joseph</namePart><namePart type="family">Kendall-Morwick</namePart><affiliation>Computer Science Department, Indiana University, Lindley Hall 215 150 S. Woodlawn Avenue, IN 47405, Bloomington, U.S.A.</affiliation><affiliation>E-mail: jmorwick@cs.indiana.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">2008</dateIssued><copyrightDate encoding="w3cdtf">2008</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: e-Science brings large-scale computation to bear on scientific problems, often by performing sequences of computational tasks organized into workflows and executed on distributed Web resources. Sophisticated AI tools have been developed to apply knowledge-rich methods to compose scientific workflows by generative planning, but the required knowledge can be difficult to acquire. Current work by the cyberinfrastructure community aims to routinely capture provenance during workflow execution, which would provide a new experience-based knowledge source for workflow generation: large-scale databases of workflow execution traces. This paper proposes exploiting these databases with a “knowledge light” approach to reuse, applying CBR methods to those traces to support scientists’ workflow generation process. This paper introduces e-Science workflows as a CBR domain, sketches key technical issues, and illustrates directions towards addressing these issues through ongoing research on Phala, a system which supports workflow generation by aiding re-use of portions of prior workflows. The paper uses workflow data collected by the myGrid and myExperiment projects in experiments which suggest that Phala’s methods have promise for assisting workflow composition in the context of scientific experimentation.</abstract><relatedItem type="host"><titleInfo><title>Advances in Case-Based Reasoning</title><subTitle>9th European Conference, ECCBR 2008, Trier, Germany, September 1-4, 2008. 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