demonstration

VFDS: An Application to Generate Fast Sample Databases

Authors:

Teodora Sandra Buda,

Thomas Cerqueus,

John Murphy,

Morten KristiansenAuthors Info & Claims

CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

Pages 2048 - 2050

https://doi.org/10.1145/2661829.2661845

Published: 03 November 2014 Publication History

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Abstract

Large amounts of data often require expensive and time-consuming analysis. Therefore, highly scalable and efficient techniques are necessary to process, analyze and discover useful information. Database sampling has proven to be a powerful method to surpass these limitations. Using only a sample of the original large database brings the benefit of obtaining useful information faster, at the potential expense of lower accuracy. In this paper, we demonstrate \vfds, a novel fast database sampling system that maintains the referential integrity of the data. The system is developed over the open-source database management system, MySQL. We present various scenarios to demonstrate the effectiveness of VFDS in approximate query answering, sample size, and execution time, on both real and synthetic databases.

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Cited By

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Cheng K(2020)A Regular Expression-based DGL for Meaningful Synthetic Data Generation2020 IEEE International Conference on Big Data and Smart Computing (BigComp)10.1109/BigComp48618.2020.00-42(396-401)Online publication date: Feb-2020
https://doi.org/10.1109/BigComp48618.2020.00-42
Zhang JTay Y(2019)A Collaborative Framework for Similarity Enforcement in Synthetic Scaling of Relational Datasets2019 IEEE 35th International Conference on Data Engineering (ICDE)10.1109/ICDE.2019.00173(1686-1689)Online publication date: Apr-2019
https://doi.org/10.1109/ICDE.2019.00173
Zhang JWang YTay Y(2018)A collaborative framework for tweaking properties in a synthetic datasetProceedings of the VLDB Endowment10.14778/3229863.323624711:12(2010-2013)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.14778/3229863.3236247
Show More Cited By

Index Terms

VFDS: An Application to Generate Fast Sample Databases
1. Information systems
  1. Data management systems

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Information & Contributors

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Published In

CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

November 2014

2152 pages

ISBN:9781450325981

DOI:10.1145/2661829

General Chairs:
Jianzhong Li
Harbin Inst. of Technology
,
X. Sean Wang
Fudan University
,
Program Chairs:
Minos Garofalakis
Technical University of Crete, Greece
,
Ian Soboroff
National Institute of Standards, USA
,
Torsten Suel
New York University, USA
,
Min Wang
Google Research, USA

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 November 2014

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Demonstration

Funding Sources

Science Foundation Ireland

Conference

CIKM '14

Sponsor:

CIKM '14: 2014 ACM Conference on Information and Knowledge Management

November 3 - 7, 2014

Shanghai, China

Acceptance Rates

CIKM '14 Paper Acceptance Rate 175 of 838 submissions, 21%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

View all

Cheng K(2020)A Regular Expression-based DGL for Meaningful Synthetic Data Generation2020 IEEE International Conference on Big Data and Smart Computing (BigComp)10.1109/BigComp48618.2020.00-42(396-401)Online publication date: Feb-2020
https://doi.org/10.1109/BigComp48618.2020.00-42
Zhang JTay Y(2019)A Collaborative Framework for Similarity Enforcement in Synthetic Scaling of Relational Datasets2019 IEEE 35th International Conference on Data Engineering (ICDE)10.1109/ICDE.2019.00173(1686-1689)Online publication date: Apr-2019
https://doi.org/10.1109/ICDE.2019.00173
Zhang JWang YTay Y(2018)A collaborative framework for tweaking properties in a synthetic datasetProceedings of the VLDB Endowment10.14778/3229863.323624711:12(2010-2013)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.14778/3229863.3236247
Zhang JTay Y(2016)DscalerProceedings of the VLDB Endowment10.14778/3007328.30073339:14(1671-1682)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.14778/3007328.3007333

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