Inferring Fine-Grained Data Provenance in Stream Data Processing: Reduced Storage Cost, High Accuracy

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

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

14 Citations (Scopus)
32 Downloads (Pure)

Abstract

Fine-grained data provenance ensures reproducibility of results in decision making, process control and e-science applications. However, maintaining this provenance is challenging in stream data processing because of its massive storage consumption, especially with large overlapping sliding windows. In this paper, we propose an approach to infer fine-grained data provenance by using a temporal data model and coarse-grained data provenance of the processing. The approach has been evaluated on a real dataset and the result shows that our proposed inferring method provides provenance information as accurate as explicit fine-grained provenance at reduced storage consumption.
Original languageUndefined
Title of host publication22nd International Conference on Database and Expert Systems Applications (DEXA 2011)
EditorsAbdelkader Hameurlain, Stephen W. Liddle, Klaus-Dieter Schewe, Xiaofang Zhou
Place of PublicationBerlin
PublisherSpringer
Pages118-127
Number of pages10
ISBN (Print)978-3-642-23090-5
DOIs
Publication statusPublished - Sep 2011

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Verlag
Volume6861
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • METIS-278792
  • IR-78048
  • Inference
  • EWI-20497
  • Stream Data
  • Storage
  • Fine grained data provenance

Cite this

Huq, M. R., Wombacher, A., & Apers, P. M. G. (2011). Inferring Fine-Grained Data Provenance in Stream Data Processing: Reduced Storage Cost, High Accuracy. In A. Hameurlain, S. W. Liddle, K-D. Schewe, & X. Zhou (Eds.), 22nd International Conference on Database and Expert Systems Applications (DEXA 2011) (pp. 118-127). (Lecture Notes in Computer Science; Vol. 6861). Berlin: Springer. https://doi.org/10.1007/978-3-642-23091-2_11