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Exploring big volume sensor data with Vroom

Summary: Vroom enables ad-hoc queries over AV sensor stores (video/LIDAR) by exploiting AV-domain properties. Selective indexing and multi-query optimization support fields-of-view reasoning and heavy feature extraction for targeted location queries. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11688
Venue
VLDB
Year
2017
Pagerank
5.6348348e-05
Overall Rank
7,355 | 49.54%
DOI
10.14778/3137765.3137783

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{moll_vldb17,
        title = {{Exploring big volume sensor data with Vroom}},
        author = {Moll, Oscar and Zalewski, Aaron and Pillai, Sudeep and Madden, Sam and Stonebraker, Michael and Gadepally, Vijay},
        journal = {PVLDB},
        series = {{VLDB} '17},
        volume = {10},
        number = {12},
        pages = {1973--1976},
        doi = {10.14778/3137765.3137783},
        url = {https://doi.org/10.14778/3137765.3137783},
        year = {2017}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
8,524 Challenges and Opportunities for Autonomous Vehicle Query Systems 2021 CIDR 5.4119882e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
132 Predicate Migration: Optimizing Queries with Expensive Predicates 1993 SIGMOD 0.00030378624
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