Facilitating Fine Grained Data Provenance using Temporal Data Model

M.R. Huq, Andreas Wombacher, Peter M.G. Apers

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

10 Citations (Scopus)

Abstract

E-science applications use fine grained data provenance to maintain the reproducibility of scientific results, i.e., for each processed data tuple, the source data used to process the tuple as well as the used approach is documented. Since most of the e-science applications perform on-line processing of sensor data using overlapping time windows, the overhead of maintaining fine grained data provenance is huge especially in longer data processing chains. This is because data items are used by many time windows. In this paper, we propose an approach to reduce storage costs for achieving fine grained data provenance by maintaining data provenance on the relation level instead on the tuple level and make the content of the used database reproducible. The approach has prototypically been implemented for streaming and manually sampled data.
Original languageUndefined
Title of host publicationProceedings of the Seventh International Workshop on Data Management for Sensor Networks, DMSN 2010
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages8-13
Number of pages6
ISBN (Print)978-1-4503-0416-0
DOIs
Publication statusPublished - 13 Sep 2010
EventSeventh International Workshop on Data Management for Sensor Networks, DMSN 2010 - Singapore, Thailand
Duration: 13 Sep 201013 Sep 2010

Publication series

Name
PublisherACM

Workshop

WorkshopSeventh International Workshop on Data Management for Sensor Networks, DMSN 2010
Period13/09/1013/09/10
Other13 Sep 2010

Keywords

  • METIS-276255
  • Fine grained data provenance
  • IR-75361
  • EWI-19179
  • E-science applications
  • Sensor data
  • CR-E
  • Temporal data model

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