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

M.R. Huq, Peter M.G. Apers, Andreas Wombacher

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)
25 Downloads (Pure)

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.
Original languageUndefined
Title of host publicationProceedings of the 24th International Conference of Scientific and Statistical Database Management (SSDBM 2012)
EditorsAnastasia Ailamaki, Shawn Bowers
Place of PublicationBerlin
PublisherSpringer
Pages397-405
Number of pages9
ISBN (Print)978-3-642-31234-2
DOIs
Publication statusPublished - Jun 2012

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Verlag
Volume7338
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • METIS-287957
  • IR-81213
  • EWI-22111
  • Data Provenance
  • Inference

Cite this

Huq, M. R., Apers, P. M. G., & Wombacher, A. (2012). Fine-Grained Provenance Inference for a Large Processing Chain with Non-materialized Intermediate Views. In A. Ailamaki, & S. Bowers (Eds.), Proceedings of the 24th International Conference of Scientific and Statistical Database Management (SSDBM 2012) (pp. 397-405). (Lecture Notes in Computer Science; Vol. 7338). Berlin: Springer. https://doi.org/10.1007/978-3-642-31235-9_26