Fine-Grained Provenance Inference for a Large Processing Chain with Non-materialized Intermediate Views


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Huq, Mohammad R. and Apers, Peter M.G. and Wombacher, Andreas (2012) Fine-Grained Provenance Inference for a Large Processing Chain with Non-materialized Intermediate Views. In: 24th International Conference of Scientific and Statistical Database Management, SSDBM 2012, 25-27 June 2012, Chania, Greece (pp. pp. 397-405).

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Abstract: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.
Item Type:Conference or Workshop Item
Copyright:© 2012 Springer
Faculty:
Electrical Engineering, Mathematics and Computer Science (EEMCS)
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Link to this item:http://purl.utwente.nl/publications/81213
Official URL:http://dx.doi.org/10.1007/978-3-642-31235-9_26
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