Facilitating Fine Grained Data Provenance using Temporal Data Model

E-science applications use ne 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 online processing of sensor data using overlapping time windows, the overhead of maintaining ne grained data provenance is huge especially in longer data processing chains. This is because data items are used by many time windows.

Provided by: VLD Digital Topic: Big Data Date Added: Sep 2010 Format: PDF

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