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

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

doi:10.1007/978-3-642-23091-2_11

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

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

Databases (Former)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-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 language	Undefined
Title of host publication	22nd International Conference on Database and Expert Systems Applications (DEXA 2011)
Editors	Abdelkader Hameurlain, Stephen W. Liddle, Klaus-Dieter Schewe, Xiaofang Zhou
Place of Publication	Berlin
Publisher	Springer
Pages	118-127
Number of pages	10
ISBN (Print)	978-3-642-23090-5
DOIs	https://doi.org/10.1007/978-3-642-23091-2_11
Publication status	Published - Sep 2011

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Verlag
Volume	6861
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Keywords

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

Access to Document

10.1007/978-3-642-23091-2_11

DEXA_2011
open

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

Huq, M.R. ; Wombacher, Andreas ; Apers, Peter M.G. / Inferring Fine-Grained Data Provenance in Stream Data Processing: Reduced Storage Cost, High Accuracy. 22nd International Conference on Database and Expert Systems Applications (DEXA 2011). editor / Abdelkader Hameurlain ; Stephen W. Liddle ; Klaus-Dieter Schewe ; Xiaofang Zhou. Berlin : Springer, 2011. pp. 118-127 (Lecture Notes in Computer Science).

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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.",

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

Inferring Fine-Grained Data Provenance in Stream Data Processing: Reduced Storage Cost, High Accuracy. / Huq, M.R.; Wombacher, Andreas; Apers, Peter M.G.

22nd International Conference on Database and Expert Systems Applications (DEXA 2011). ed. / Abdelkader Hameurlain; Stephen W. Liddle; Klaus-Dieter Schewe; Xiaofang Zhou. Berlin : Springer, 2011. p. 118-127 (Lecture Notes in Computer Science; Vol. 6861).

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

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AB - 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.

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Huq MR, Wombacher A, Apers PMG. Inferring Fine-Grained Data Provenance in Stream Data Processing: Reduced Storage Cost, High Accuracy. In Hameurlain A, Liddle SW, Schewe K-D, Zhou X, editors, 22nd International Conference on Database and Expert Systems Applications (DEXA 2011). Berlin: Springer. 2011. p. 118-127. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-642-23091-2_11