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EVA: A Symbolic Approach to Accelerating Exploratory Video Analytics with Materialized Views
Summary: EVA is a VDBMS that materializes and reuses UDF results to speed exploratory analytics. Symbolic predicate overlap analysis, UDF reuse, and cost-aware optimization for order and model choice, delivering 4x speed with negligible storage (1.001x).
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
h38f1f0d63af134a0
Venue
SIGMOD
Year
2022
Pagerank
8.0158999e-05
Overall Rank
2,787 | 81.27%
DOI
10.1145/3514221.3526142
Incoming Non-self Citations Over Time
BibTeX Citation
Copy BibTeX
@inproceedings{xu_sigmod22,
title = {{EVA: A Symbolic Approach to Accelerating Exploratory Video Analytics with Materialized Views}},
author = {Xu, Zhuangdi and Kakkar, Gaurav Tarlok and Arulraj, Joy and Ramachandran, Umakishore},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3526142},
url = {https://dl.acm.org/doi/10.1145/3514221.3526142},
year = {2022}
}
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Pagerank
37
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1996
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1993
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2005
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2000
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2003
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271
NoScope: Optimizing Neural Network Queries over Video at Scale
2017
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281
Accelerating Machine Learning Inference with Probabilistic Predicates
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541
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2020
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552
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2001
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790
Cosette: An Automated Prover for SQL
2017
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1,131
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2007
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1,378
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1,745
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2018
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9.7343818e-05
1,843
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ReStore: Reusing Results of MapReduce Jobs
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9.3687009e-05
2,032
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1994
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2002
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3,419
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2021
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2017
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