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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
6516
Venue
SIGMOD
Year
2022
Pagerank
7.9474026e-05
Overall Rank
2,933 | 79.88%
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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Improved Histograms for Selectivity Estimation of Range Predicates
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Efficient and Extensible Algorithms for Multi Query Optimization
2000
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1993
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2007
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2018
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9.8079546e-05
1,816
A Performance Study of Query Optimization Algorithms on a Database System Supporting Procedures
1988
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