research-article

Facilitating fine grained data provenance using temporal data model

Authors:

Mohammad R. Huq,

Andreas Wombacher,

Peter M. G. ApersAuthors Info & Claims

DMSN '10: Proceedings of the Seventh International Workshop on Data Management for Sensor Networks

Pages 8 - 13

https://doi.org/10.1145/1858158.1858163

Published: 13 September 2010 Publication History

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Abstract

E-science applications use fine grained data provenance to maintain the reproducibility of scientific results, i.e., for each processed data tuple, the source data used to process the tuple as well as the used approach is documented. Since most of the e-science applications perform on-line processing of sensor data using overlapping time windows, the overhead of maintaining fine grained data provenance is huge especially in longer data processing chains. This is because data items are used by many time windows. In this paper, we propose an approach to reduce storage costs for achieving fine grained data provenance by maintaining data provenance on the relation level instead on the tuple level and make the content of the used database reproducible. The approach has prototypically been implemented for streaming and manually sampled data.

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Cited By

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Sansrimahachai W(2016)Provenance in Web Feed Mash-Up SystemsInternational Journal of Information Technology and Web Engineering10.4018/IJITWE.201610010311:4(43-62)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.4018/IJITWE.2016100103
Glavic BSheykh Esmaili KFischer PTatbul NChakravarthy SUrban SPietzuch PRundensteiner E(2013)AriadneProceedings of the 7th ACM international conference on Distributed event-based systems10.1145/2488222.2488256(39-50)Online publication date: 29-Jun-2013
https://dl.acm.org/doi/10.1145/2488222.2488256
Huq MApers PWombacher A(2013)An Inference-Based Framework to Manage Data Provenance in Geoscience ApplicationsIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2013.224776951:11(5113-5130)Online publication date: Nov-2013
https://doi.org/10.1109/TGRS.2013.2247769
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Index Terms

Facilitating fine grained data provenance using temporal data model
1. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language features
        Data types and structures

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Published In

DMSN '10: Proceedings of the Seventh International Workshop on Data Management for Sensor Networks

September 2010

45 pages

ISBN:9781450304160

DOI:10.1145/1858158

Conference Chairs:
Demetris Zeinalipour
University of Cyprus, Nicosia, Cypru
,
Wang-Chien Lee
The Pennsylvania State University, University Park, PA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 13 September 2010

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DMSN '10: 7th International Workshop on Data Management for Sensor Networks

September 13, 2010

Singapore

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Overall Acceptance Rate 6 of 16 submissions, 38%

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Cited By

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Sansrimahachai W(2016)Provenance in Web Feed Mash-Up SystemsInternational Journal of Information Technology and Web Engineering10.4018/IJITWE.201610010311:4(43-62)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.4018/IJITWE.2016100103
Glavic BSheykh Esmaili KFischer PTatbul NChakravarthy SUrban SPietzuch PRundensteiner E(2013)AriadneProceedings of the 7th ACM international conference on Distributed event-based systems10.1145/2488222.2488256(39-50)Online publication date: 29-Jun-2013
https://dl.acm.org/doi/10.1145/2488222.2488256
Huq MApers PWombacher A(2013)An Inference-Based Framework to Manage Data Provenance in Geoscience ApplicationsIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2013.224776951:11(5113-5130)Online publication date: Nov-2013
https://doi.org/10.1109/TGRS.2013.2247769
Sansrimahachai WMoreau LWeal M(2013)An on-the-fly provenance tracking mechanism for stream processing systems2013 IEEE/ACIS 12th International Conference on Computer and Information Science (ICIS)10.1109/ICIS.2013.6607885(475-481)Online publication date: Jun-2013
https://doi.org/10.1109/ICIS.2013.6607885
Wombacher AHuq M(2013)Towards Automatic Capturing of Semi-structured Process ProvenanceData-Driven Process Discovery and Analysis10.1007/978-3-642-40919-6_5(84-99)Online publication date: 2013
https://doi.org/10.1007/978-3-642-40919-6_5
Huq MWombacher AApers PWada Y(2012)From scripts towards provenance inferenceProceedings of the 2012 IEEE 8th International Conference on E-Science (e-Science)10.1109/eScience.2012.6404467(1-8)Online publication date: 8-Oct-2012
https://dl.acm.org/doi/10.1109/eScience.2012.6404467
Chirigati FFreire J(2012)Towards integrating workflow and database provenanceProceedings of the 4th international conference on Provenance and Annotation of Data and Processes10.1007/978-3-642-34222-6_2(11-23)Online publication date: 19-Jun-2012
https://dl.acm.org/doi/10.1007/978-3-642-34222-6_2
Huq MApers PWombacher A(2012)Probabilistic Inference of Fine-Grained Data ProvenanceDatabase and Expert Systems Applications10.1007/978-3-642-32600-4_22(296-310)Online publication date: 2012
https://doi.org/10.1007/978-3-642-32600-4_22
Huq MApers PWombacher A(2012)Fine-grained provenance inference for a large processing chain with non-materialized intermediate viewsProceedings of the 24th international conference on Scientific and Statistical Database Management10.1007/978-3-642-31235-9_26(397-405)Online publication date: 25-Jun-2012
https://dl.acm.org/doi/10.1007/978-3-642-31235-9_26
Huq MWombacher AApers P(2011)Inferring fine-grained data provenance in stream data processingProceedings of the 22nd international conference on Database and expert systems applications - Volume Part II10.5555/2033546.2033560(118-127)Online publication date: 29-Aug-2011
https://dl.acm.org/doi/10.5555/2033546.2033560
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