research-article

Exploring big volume sensor data with vroom

Authors:

Vijay GadepallyAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 10, Issue 12

Pages 1973 - 1976

https://doi.org/10.14778/3137765.3137822

Published: 01 August 2017 Publication History

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Abstract

State of the art sensors within a single autonomous vehicle (AV) can produce video and LIDAR data at rates greater than 30 GB/hour. Unsurprisingly, even small AV research teams can accumulate tens of terabytes of sensor data from multiple trips and multiple vehicles. AV practitioners would like to extract information about specific locations or specific situations for further study, but are often unable to. Queries over AV sensor data are different from generic analytics or spatial queries because they demand reasoning about fields of view as well as heavy computation to extract features from scenes. In this article and demo we present Vroom, a system for ad-hoc queries over AV sensor databases. Vroom combines domain specific properties of AV datasets with selective indexing and multi-query optimization to address challenges posed by AV sensor data.

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Cited By

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Cong PTam NYin HZheng BStantic BHung N(2019)Efficient User Guidance for Validating Participatory Sensing DataACM Transactions on Intelligent Systems and Technology10.1145/332616410:4(1-30)Online publication date: 17-Jul-2019
https://dl.acm.org/doi/10.1145/3326164
Zhang RGuo JZhou YJiang HWang C(2019)traj2bitsProceedings of the ACM Turing Celebration Conference - China10.1145/3321408.3321578(1-5)Online publication date: 17-May-2019
https://dl.acm.org/doi/10.1145/3321408.3321578

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cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 10, Issue 12

August 2017

427 pages

ISSN:2150-8097

Editors:
Peter Boncz
CWI
,
Ken Salem
University of Waterloo

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VLDB Endowment

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Published: 01 August 2017

Published in PVLDB Volume 10, Issue 12

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Cited By

View all

Cong PTam NYin HZheng BStantic BHung N(2019)Efficient User Guidance for Validating Participatory Sensing DataACM Transactions on Intelligent Systems and Technology10.1145/332616410:4(1-30)Online publication date: 17-Jul-2019
https://dl.acm.org/doi/10.1145/3326164
Zhang RGuo JZhou YJiang HWang C(2019)traj2bitsProceedings of the ACM Turing Celebration Conference - China10.1145/3321408.3321578(1-5)Online publication date: 17-May-2019
https://dl.acm.org/doi/10.1145/3321408.3321578

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Secure and Efficient Big Data Gathering in Heterogeneous Wireless Sensor Networks

From Copernicus Big Data to Big Information and Big Knowledge: A Demo from the Copernicus App Lab Project

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