A Dataflow-Oriented Atomicity and Provenance System for Pipelined Scientific Workflows
Identifieur interne : 000219 ( Istex/Corpus ); précédent : 000218; suivant : 000220A Dataflow-Oriented Atomicity and Provenance System for Pipelined Scientific Workflows
Auteurs : Liqiang Wang ; Shiyong Lu ; Xubo Fei ; Jeffrey RamSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2007.
Abstract
Abstract: Scientific workflows have gained great momentum in recent years due to their critical roles in e-Science and cyberinfrastructure applications. However, some tasks of a scientific workflow might fail during execution. A domain scientist might require a region of a scientific workflow to be “atomic”. Data provenance, which determines the source data that are used to produce a data item, is also essential to scientific workflows. In this paper, we propose: (i) an architecture for scientific workflow management systems that supports both provenance and atomicity; (ii) a dataflow-oriented atomicity model that supports the notions of commit and abort; and (iii) a dataflow-oriented provenance model that, in addition to supporting existing provenance graphs and queries, also supports queries related to atomicity and failure.
Url:
DOI: 10.1007/978-3-540-72588-6_42
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<ChapterTitle Language="En">A Dataflow-Oriented Atomicity and Provenance System for Pipelined Scientific Workflows</ChapterTitle>
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<Affiliation ID="Aff2"><OrgName>Dept. of Computer Science, Wayne State University</OrgName>
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<Para>Scientific workflows have gained great momentum in recent years due to their critical roles in e-Science and cyberinfrastructure applications. However, some tasks of a scientific workflow might fail during execution. A domain scientist might require a region of a scientific workflow to be “atomic”. Data provenance, which determines the source data that are used to produce a data item, is also essential to scientific workflows. In this paper, we propose: (<Emphasis Type="Italic">i</Emphasis>
) an architecture for scientific workflow management systems that supports both provenance and atomicity; (<Emphasis Type="Italic">ii</Emphasis>
) a dataflow-oriented atomicity model that supports the notions of commit and abort; and (<Emphasis Type="Italic">iii</Emphasis>
) a dataflow-oriented provenance model that, in addition to supporting existing provenance graphs and queries, also supports queries related to atomicity and failure.</Para>
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<abstract lang="en">Abstract: Scientific workflows have gained great momentum in recent years due to their critical roles in e-Science and cyberinfrastructure applications. However, some tasks of a scientific workflow might fail during execution. A domain scientist might require a region of a scientific workflow to be “atomic”. Data provenance, which determines the source data that are used to produce a data item, is also essential to scientific workflows. In this paper, we propose: (i) an architecture for scientific workflow management systems that supports both provenance and atomicity; (ii) a dataflow-oriented atomicity model that supports the notions of commit and abort; and (iii) a dataflow-oriented provenance model that, in addition to supporting existing provenance graphs and queries, also supports queries related to atomicity and failure.</abstract>
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