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)
Incoming Non-self Citations Over Time
Authors
- 1. Oscar Moll (Massachusetts Institute of Technology)
- 2. Aaron Zalewski (Massachusetts Institute of Technology)
- 3. Sudeep Pillai (Massachusetts Institute of Technology)
- 4. Sam Madden (Massachusetts Institute of Technology)
- 5. Michael Stonebraker (Massachusetts Institute of Technology)
- 6. Vijay Gadepally (Massachusetts Institute of Technology)
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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