research-article

Using VDMS to index and search 100M images

Authors:

Chaunté W. LacewellAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 14, Issue 12

Pages 3240 - 3252

https://doi.org/10.14778/3476311.3476381

Published: 01 July 2021 Publication History

Abstract

Data scientists spend most of their time dealing with data preparation, rather than doing what they know best: build machine learning models and algorithms to solve previously unsolvable problems. In this paper, we describe the Visual Data Management System (VDMS), and demonstrate how it can be used to simplify the data preparation process and consequently gain in efficiency simply because we are using a system designed for the job. To demonstrate this, we use one of the largest available public datasets (YFCC100M), with 100 million images and videos, plus additional data including machine-generated tags, for a total of about ~12TB of data. VDMS differs from existing data management systems due to its focus on supporting machine learning and data analytics pipelines that rely on images, videos, and feature vectors, treating these as first class citizens. We demonstrate how VDMS outperforms well-known and widely used systems for data management by up to ~364x, with an average improvement of about 85x for our use-cases, and particularly at scale, for a image search engine implementation. At the same time, VDMS simplifies the process of data preparation and data access, and provides functionalities non-existent in alternative options.

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Cited By

Sirin UIdreos S(2024)The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage FormatProceedings of the ACM on Management of Data10.1145/36393072:1(1-31)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639307
Zheng YFixelle JHuo PStan MMesnier MNarayanan VEgger BLee D(2023)ISVABI: In-Storage Video Analytics Engine with Block InterfaceProceedings of the 24th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems10.1145/3589610.3596275(111-121)Online publication date: 13-Jun-2023
https://dl.acm.org/doi/10.1145/3589610.3596275

Index Terms

Using VDMS to index and search 100M images
1. Information systems
  1. Information retrieval
  2. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 14, Issue 12

July 2021

587 pages

ISSN:2150-8097

Editors:
Xin Luna Dong
Amazon
,
Felix Naumann
HPI, University of Potsdam

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VLDB Endowment

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Published: 01 July 2021

Published in PVLDB Volume 14, Issue 12

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Cited By

Sirin UIdreos S(2024)The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage FormatProceedings of the ACM on Management of Data10.1145/36393072:1(1-31)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639307
Zheng YFixelle JHuo PStan MMesnier MNarayanan VEgger BLee D(2023)ISVABI: In-Storage Video Analytics Engine with Block InterfaceProceedings of the 24th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems10.1145/3589610.3596275(111-121)Online publication date: 13-Jun-2023
https://dl.acm.org/doi/10.1145/3589610.3596275

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