Handle URI:
http://hdl.handle.net/10754/598228
Title:
Logical provenance in data-oriented workflows?
Authors:
Ikeda, R.; Das Sarma, Akash; Widom, J.
Abstract:
We consider the problem of defining, generating, and tracing provenance in data-oriented workflows, in which input data sets are processed by a graph of transformations to produce output results. We first give a new general definition of provenance for general transformations, introducing the notions of correctness, precision, and minimality. We then determine when properties such as correctness and minimality carry over from the individual transformations' provenance to the workflow provenance. We describe a simple logical-provenance specification language consisting of attribute mappings and filters. We provide an algorithm for provenance tracing in workflows where logical provenance for each transformation is specified using our language. We consider logical provenance in the relational setting, observing that for a class of Select-Project-Join (SPJ) transformations, logical provenance specifications encode minimal provenance. We have built a prototype system supporting the features and algorithms presented in the paper, and we report a few preliminary experimental results. © 2013 IEEE.
Citation:
Ikeda R, Das Sarma A, Widom J (2013) Logical provenance in data-oriented workflows? 2013 IEEE 29th International Conference on Data Engineering (ICDE). Available: http://dx.doi.org/10.1109/ICDE.2013.6544882.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2013 IEEE 29th International Conference on Data Engineering (ICDE)
Issue Date:
Apr-2013
DOI:
10.1109/ICDE.2013.6544882
Type:
Conference Paper
Sponsors:
This work was supported by the National Science Foundation (IIS-0904497), the Boeing Corporation, and a KAUST research grant.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorIkeda, R.en
dc.contributor.authorDas Sarma, Akashen
dc.contributor.authorWidom, J.en
dc.date.accessioned2016-02-25T13:40:01Zen
dc.date.available2016-02-25T13:40:01Zen
dc.date.issued2013-04en
dc.identifier.citationIkeda R, Das Sarma A, Widom J (2013) Logical provenance in data-oriented workflows? 2013 IEEE 29th International Conference on Data Engineering (ICDE). Available: http://dx.doi.org/10.1109/ICDE.2013.6544882.en
dc.identifier.doi10.1109/ICDE.2013.6544882en
dc.identifier.urihttp://hdl.handle.net/10754/598228en
dc.description.abstractWe consider the problem of defining, generating, and tracing provenance in data-oriented workflows, in which input data sets are processed by a graph of transformations to produce output results. We first give a new general definition of provenance for general transformations, introducing the notions of correctness, precision, and minimality. We then determine when properties such as correctness and minimality carry over from the individual transformations' provenance to the workflow provenance. We describe a simple logical-provenance specification language consisting of attribute mappings and filters. We provide an algorithm for provenance tracing in workflows where logical provenance for each transformation is specified using our language. We consider logical provenance in the relational setting, observing that for a class of Select-Project-Join (SPJ) transformations, logical provenance specifications encode minimal provenance. We have built a prototype system supporting the features and algorithms presented in the paper, and we report a few preliminary experimental results. © 2013 IEEE.en
dc.description.sponsorshipThis work was supported by the National Science Foundation (IIS-0904497), the Boeing Corporation, and a KAUST research grant.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.titleLogical provenance in data-oriented workflows?en
dc.typeConference Paperen
dc.identifier.journal2013 IEEE 29th International Conference on Data Engineering (ICDE)en
dc.contributor.institutionStanford University, Palo Alto, United Statesen
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