Adaptive Inference of Fine-grained Data Provenance to Achieve High Accuracy at Lower Storage Costs

Mohammad Rezwanul Huq; Andreas  Wombacher; Peter M.G. Apers

doi:10.1109/eScience.2011.36

Adaptive Inference of Fine-grained Data Provenance to Achieve High Accuracy at Lower Storage Costs

Mohammad Rezwanul Huq, Andreas Wombacher, Peter M.G. Apers

Databases (Former)

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

10 Citations (Scopus)

Abstract

In stream data processing, data arrives continuously and is processed by decision making, process control and e-science applications. 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 techniques which can significantly reduce storage costs and can achieve high accuracy. Our evaluation shows that adaptive inference technique can achieve almost 100% accurate provenance information for a given dataset at lower storage costs than the other techniques. Moreover, we present a guideline about the usage of different provenance collection techniques described in this paper based on the transformation operation and stream characteristics.

Original language	English
Title of host publication	7th IEEE International Conference on E-Science, e-Science 2011
Place of Publication	Piscataway, NJ
Publisher	IEEE Computer Society
Pages	202-209
Number of pages	8
ISBN (Electronic)	978-0-7695-4597-4
ISBN (Print)	978-1-4577-2163-2
DOIs	https://doi.org/10.1109/eScience.2011.36
Publication status	Published - Dec 2011
Event	7th IEEE International Conference on e-Science 2011 - Stockholm, Sweden Duration: 5 Dec 2011 → 8 Dec 2011 Conference number: 7

Conference

Conference	7th IEEE International Conference on e-Science 2011
Abbreviated title	e-Science
Country	Sweden
City	Stockholm
Period	5/12/11 → 8/12/11

Keywords

METIS-285072
IR-79577
Inference
EWI-21400
Stream Data
Storage
Fine grained data provenance

Access to Document

10.1109/eScience.2011.36

Cite this

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title = "Adaptive Inference of Fine-grained Data Provenance to Achieve High Accuracy at Lower Storage Costs",

abstract = "In stream data processing, data arrives continuously and is processed by decision making, process control and e-science applications. 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 techniques which can significantly reduce storage costs and can achieve high accuracy. Our evaluation shows that adaptive inference technique can achieve almost 100% accurate provenance information for a given dataset at lower storage costs than the other techniques. Moreover, we present a guideline about the usage of different provenance collection techniques described in this paper based on the transformation operation and stream characteristics.",

keywords = "METIS-285072, IR-79577, Inference, EWI-21400, Stream Data, Storage, Fine grained data provenance",

author = "Huq, {Mohammad Rezwanul} and Andreas Wombacher and Apers, {Peter M.G.}",

note = "eemcs-eprint-21400 ; 7th IEEE International Conference on e-Science 2011, e-Science ; Conference date: 05-12-2011 Through 08-12-2011",

year = "2011",

month = dec,

doi = "10.1109/eScience.2011.36",

language = "English",

isbn = "978-1-4577-2163-2",

pages = "202--209",

booktitle = "7th IEEE International Conference on E-Science, e-Science 2011",

publisher = "IEEE Computer Society",

address = "United States",

}

Huq, MR, Wombacher, A & Apers, PMG 2011, Adaptive Inference of Fine-grained Data Provenance to Achieve High Accuracy at Lower Storage Costs. in 7th IEEE International Conference on E-Science, e-Science 2011. IEEE Computer Society, Piscataway, NJ, pp. 202-209, 7th IEEE International Conference on e-Science 2011, Stockholm, Sweden, 5/12/11. https://doi.org/10.1109/eScience.2011.36

Adaptive Inference of Fine-grained Data Provenance to Achieve High Accuracy at Lower Storage Costs. / Huq, Mohammad Rezwanul; Wombacher, Andreas ; Apers, Peter M.G.

7th IEEE International Conference on E-Science, e-Science 2011. Piscataway, NJ : IEEE Computer Society, 2011. p. 202-209.

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

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N2 - In stream data processing, data arrives continuously and is processed by decision making, process control and e-science applications. 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 techniques which can significantly reduce storage costs and can achieve high accuracy. Our evaluation shows that adaptive inference technique can achieve almost 100% accurate provenance information for a given dataset at lower storage costs than the other techniques. Moreover, we present a guideline about the usage of different provenance collection techniques described in this paper based on the transformation operation and stream characteristics.

AB - In stream data processing, data arrives continuously and is processed by decision making, process control and e-science applications. 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 techniques which can significantly reduce storage costs and can achieve high accuracy. Our evaluation shows that adaptive inference technique can achieve almost 100% accurate provenance information for a given dataset at lower storage costs than the other techniques. Moreover, we present a guideline about the usage of different provenance collection techniques described in this paper based on the transformation operation and stream characteristics.

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KW - IR-79577

KW - Inference

KW - EWI-21400

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KW - Storage

KW - Fine grained data provenance

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M3 - Conference contribution

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BT - 7th IEEE International Conference on E-Science, e-Science 2011

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Y2 - 5 December 2011 through 8 December 2011

ER -

Adaptive Inference of Fine-grained Data Provenance to Achieve High Accuracy at Lower Storage Costs

Abstract

Conference

Keywords

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Cite this