Probabilistic inference of fine-grained data provenance

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

doi:10.1007/978-3-642-32600-4_22

Probabilistic inference of fine-grained data provenance

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

Databases (Former)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

2 Citations (Scopus)

44 Downloads (Pure)

Abstract

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 language	Undefined
Title of host publication	23rd International Conference on Database and Expert System Applications (DEXA 2012)
Place of Publication	Berlin
Publisher	Springer
Pages	296-310
Number of pages	15
ISBN (Print)	978-3-642-32599-1
DOIs	https://doi.org/10.1007/978-3-642-32600-4_22
Publication status	Published - Sept 2012
Event	23rd International Conference on Database and Expert Syetem Applications, DEXA 2012 - Vienna, Austria Duration: 3 Sept 2012 → 6 Sept 2012 Conference number: 23

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Verlag
Volume	7446
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	23rd International Conference on Database and Expert Syetem Applications, DEXA 2012
Abbreviated title	DEXA
Country/Territory	Austria
City	Vienna
Period	3/09/12 → 6/09/12

Keywords

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

Access to Document

10.1007/978-3-642-32600-4_22

fulltext-40

Cite this

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title = "Probabilistic inference of fine-grained data provenance",

abstract = "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.",

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

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Huq, MR, Apers, PMG & Wombacher, A 2012, Probabilistic inference of fine-grained data provenance. in 23rd International Conference on Database and Expert System Applications (DEXA 2012). Lecture Notes in Computer Science, vol. 7446, Springer, Berlin, pp. 296-310, 23rd International Conference on Database and Expert Syetem Applications, DEXA 2012, Vienna, Austria, 3/09/12. https://doi.org/10.1007/978-3-642-32600-4_22

Probabilistic inference of fine-grained data provenance. / Huq, M.R.; Apers, Peter M.G.; Wombacher, Andreas.
23rd International Conference on Database and Expert System Applications (DEXA 2012). Berlin: Springer, 2012. p. 296-310 (Lecture Notes in Computer Science; Vol. 7446).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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