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KEN: An Execution Engine for Unstructured Database Systems

Summary: KEN is a dedicated execution engine for unstructured DBMSs that dynamically deploys model cascades to balance query accuracy and latency. GPU-aware placement and invocation scheduling mitigate cascade overhead, yielding up to 122× lower latency than baseline execution. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
14619
Venue
VLDB
Year
2026
Pagerank
5.0723324e-05
Overall Rank
10,707 | 26.80%
DOI
10.14778/3796195.3796204

Incoming Non-self Citations Over Time

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Authors

BibTeX Citation

@article{kossmann_vldb26,
        title = {{KEN: An Execution Engine for Unstructured Database Systems}},
        author = {Kossmann, Ferdi and Wu, Ziniu and Turk, Alex and Tatbul, Nesime and Cao, Lei and Madden, Samuel},
        journal = {PVLDB},
        series = {{VLDB} '26},
        volume = {19},
        number = {5},
        pages = {902--916},
        doi = {10.14778/3796195.3796204},
        url = {https://doi.org/10.14778/3796195.3796204},
        year = {2026}
}

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Outgoing Citations (Sorted by Pagerank)

Showing 21 of 21 cited papers.

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

Rank Cited Paper Year Venue Pagerank
284 NoScope: Optimizing Neural Network Queries over Video at Scale 2017 VLDB 0.00022304472
295 Accelerating Machine Learning Inference with Probabilistic Predicates 2018 SIGMOD 0.00022170662
1,049 MIRIS: Fast Object Track Queries in Video 2020 SIGMOD 0.00012401048
1,178 DocETL: Agentic Query Rewriting and Evaluation for Complex Document Processing 2025 VLDB 0.00011775585
2,942 EVA: A Symbolic Approach to Accelerating Exploratory Video Analytics with Materialized Views 2022 SIGMOD 7.9141635e-05
3,442 A Demonstration of Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference 2020 VLDB 7.3944009e-05
3,541 Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures 2023 VLDB 7.3132463e-05
3,777 Vaas: Video Analytics At Scale 2020 VLDB 7.1204909e-05
4,017 Optimizing Video Analytics with Declarative Model Relationships 2023 VLDB 6.9341165e-05
4,062 Abacus: A Cost-Based Optimizer for Semantic Operator Systems 2026 VLDB 6.9089923e-05
4,113 Rafiki: Machine Learning as an Analytics Service System 2019 VLDB 6.8719145e-05
4,314 OTIF: Efficient Tracker Pre-processing over Large Video Datasets 2022 SIGMOD 6.7467174e-05
5,132 Stage: Query Execution Time Prediction in Amazon Redshift 2024 SIGMOD 6.3357697e-05
5,219 Databases Unbound: Querying All of the World’s Bytes with AI 2024 VLDB 6.2976511e-05
5,816 Seiden: Revisiting Query Processing in Video Database Systems 2023 VLDB 6.0619793e-05
6,522 Extract-Transform-Load for Video Streams 2023 VLDB 5.8394282e-05
6,741 Serving Deep Learning Models with Deduplication from Relational Databases 2022 VLDB 5.7722187e-05
7,104 Aero: Adaptive Query Processing of ML Queries 2025 SIGMOD 5.6883134e-05
7,855 Biathlon: Harnessing Model Resilience for Accelerating ML Inference Pipelines 2024 VLDB 5.5147783e-05
9,033 Optimizing Inference Serving on Serverless Platforms 2022 VLDB 5.3127398e-05
9,603 Declarative Data Serving: The Future of Machine Learning Inference on the Edge 2021 VLDB 5.2308428e-05
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