Fine-grained data provenance ensures reproducibility of results in decision making, process control and e-science applications. However, maintaining this provenance is challenging in stream data processing because of its massive storage consumption, especially with large overlapping sliding windows. In this paper, we propose an approach to infer fine-grained data provenance by using a temporal data model and coarse-grained data provenance of the processing. The approach has been evaluated on a real dataset and the result shows that our proposed inferring method provides provenance information as accurate as explicit fine-grained provenance at reduced storage consumption.
|Name||Lecture Notes in Computer Science|
|Conference||22nd International Conference on Database and Expert Systems Applications, DEXA 2011|
|Period||29/08/11 → 2/09/11|
|Other||29 Aug - 02 Sep 2011|
- Stream Data
- Fine grained data provenance