Fine-grained provenance inference for a large processing chain with non-materialized intermediate views

Authors:
Mohammad Rezwanul Huq;Peter M. G. Apers;Andreas Wombacher
Affiliations:
University of Twente, Enschede, The Netherlands;University of Twente, Enschede, The Netherlands;University of Twente, Enschede, The Netherlands
Venue:
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Year:
2012

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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.