Probabilistic inference of fine-grained data provenance

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

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

2 Citations (Scopus)
44 Downloads (Pure)


Decision making, process control and e-science applications process stream data, mostly produced by sensors. To control and monitor these applications, reproducibility of result is a vital requirement. However, it requires massive amount of storage space to store fine-grained provenance data especially for those transformations with overlapping sliding windows. In this paper, we propose a probabilistic technique to infer fine-grained provenance which can also estimate the accuracy beforehand. Our evaluation shows that the probabilistic inference technique achieves same level of accuracy as the other approaches do, with minimal prior knowledge.
Original languageUndefined
Title of host publication23rd International Conference on Database and Expert System Applications (DEXA 2012)
Place of PublicationBerlin
Number of pages15
ISBN (Print)978-3-642-32599-1
Publication statusPublished - Sept 2012
Event23rd International Conference on Database and Expert Syetem Applications, DEXA 2012 - Vienna, Austria
Duration: 3 Sept 20126 Sept 2012
Conference number: 23

Publication series

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


Conference23rd International Conference on Database and Expert Syetem Applications, DEXA 2012
Abbreviated titleDEXA


  • METIS-289740
  • Markov Chain
  • IR-83374
  • Inference
  • EWI-22387
  • Probabilistic Approach
  • MSC-42A61
  • CR-H.2.8
  • Data Provenance

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