@inproceedings{b38fb5edb1ae4bb98a46ca6ca95e4aa9,
title = "Fine-Grained Provenance Inference for a Large Processing Chain with Non-materialized Intermediate Views",
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.",
keywords = "METIS-287957, IR-81213, EWI-22111, Data Provenance, Inference",
author = "M.R. Huq and Apers, {Peter M.G.} and Andreas Wombacher",
note = "10.1007/978-3-642-31235-9_26 ; 24th International Conference of Scientific and Statistical Database Management, SSDBM 2012 ; Conference date: 25-06-2012 Through 27-06-2012",
year = "2012",
month = jun,
doi = "10.1007/978-3-642-31235-9_26",
language = "Undefined",
isbn = "978-3-642-31234-2",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "397--405",
editor = "Anastasia Ailamaki and Shawn Bowers",
booktitle = "Proceedings of the 24th International Conference of Scientific and Statistical Database Management (SSDBM 2012)",
address = "Germany",
}