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

15 Citations (Scopus)
63 Downloads (Pure)


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
Number of pages10
ISBN (Print)978-3-642-23090-5
Publication statusPublished - Sept 2011
Event22nd International Conference on Database and Expert Systems Applications, DEXA 2011 - Toulouse, France
Duration: 29 Aug 20112 Sept 2011

Publication series

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


Conference22nd International Conference on Database and Expert Systems Applications, DEXA 2011
Other29 Aug - 02 Sep 2011


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

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