Abstract

In this paper, we look at how we can leverage Spark platform for efficiently processing fine-grained provenance queries on large volumes of workflow provenance data. Simple recursive querying based Spark solutions involve large data scanning cost and hence do not work well. We propose a novel provenance framework which is engineered to quickly determine a small volume of data containing the entire lineage of the queried data-item. This small volume of data is then recursively processed to figure out the provenance of the queried data-item. We study the effectiveness of the proposed framework on a provenance trace obtained from a financial domain text curation workflow and report our observations. We show that the proposed framework easily outperforms the naive approaches.

Full Text

Submitted Version (Free)

Published Version

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.