Towards web-scale how-provenance

Daniel Deutch, Amir Gilad, Yuval Moskovitch

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The annotation of data with meta-data, and its propagation through data-intensive computation in a way that follows the transformations that the data undergoes ('how-provenance'), has many applications, including explanation of the computation results, assessing their trustworthiness and proving their correctness, evaluation in presence of incomplete or probabilistic information, view maintenance, etc. As data gets bigger, its transformations become more complex, and both are being relegated to the cloud, the role of provenance in these applications is even more crucial. But at the same time, the overhead incurred due to provenance computation, in terms of time, space and communication, may limit the scalability of how-provenance management systems. We envision an approach for addressing this complex problem, through allowing selective tracking of how-provenance, where the selection criteria are partly based on the meta-data itself. We illustrate use-cases in the web context, and highlight some challenges in this respect.

Original languageEnglish
Title of host publicationICDEW 2015 - 2015 IEEE 31st International Conference on Data Engineering Workshops
PublisherIEEE Computer Society
Number of pages3
ISBN (Electronic)9781479984411
StatePublished - 19 Jun 2015
Event2015 31st IEEE International Conference on Data Engineering Workshops, ICDEW 2015 - Seoul, Korea, Republic of
Duration: 13 Apr 201517 Apr 2015

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627


Conference2015 31st IEEE International Conference on Data Engineering Workshops, ICDEW 2015
Country/TerritoryKorea, Republic of


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