Many applications facilitate a data processing chain, i.e. a workflow, to process data. Results of intermediate processing steps may not be persistent since reproducing these results are not costly and these are hardly re-usable. However, in stream data processing where data arrives continuously, documenting fine-grained provenance explicitly for a processing chain to reproduce results is not a feasible solution since the provenance data may become a multiple of the actual sensor data. In this paper, we propose the multi-step provenance inference technique that infers provenance data for the entire workflow with non-materialized intermediate views. Our solution provides high quality provenance graph.
|Name||Lecture Notes in Computer Science|
|Conference||24th International Conference of Scientific and Statistical Database Management, SSDBM 2012|
|Period||25/06/12 → 27/06/12|
|Other||25-27 June 2012|
- Data Provenance