Facilitating fine grained data provenance using temporal data model

Authors:
Mohammad R. Huq;Andreas Wombacher;Peter M. G. Apers
Affiliations:
University of Twente, Enschede, The Netherlands;University of Twente, Enschede, The Netherlands;University of Twente, Enschede, The Netherlands
Venue:
Proceedings of the Seventh International Workshop on Data Management for Sensor Networks
Year:
2010

Citing 7
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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.