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)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Ferdi Kossmann (Massachusetts Institute of Technology)
- 2. Ziniu Wu (Massachusetts Institute of Technology)
- 3. Alex Turk (Intel)
- 4. Nesime Tatbul (Intel; Massachusetts Institute of Technology)
- 5. Lei Cao (University of Arizona)
- 6. Samuel Madden (Massachusetts Institute of Technology)
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}
}Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
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.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 10,425 | KathDB: Explainable Multimodal Database Management System with Human-AI Collaboration | 2026 | CIDR |
| 2 | 11,239 | VQLens: A Demonstration of Vector Query Execution Analysis | 2025 | SIGMOD |
| 3 | 11,078 | iPDB: SQL with ML and LLM Predicates (Towards a Database Engine for AI) | 2026 | VLDB |
| 4 | 10,193 | Structure-Aware Machine Learning over Multi-Relational Databases | 2021 | SIGMOD |
| 5 | 4,583 | QUEST: Query Optimization in Unstructured Document Analysis | 2025 | VLDB |
| 6 | 11,043 | Demonstrating GenDB: Instance-Optimized and Customized Query Processing Code Generation via LLM Agents | 2026 | VLDB |
| 7 | 3,717 | Logical and Physical Optimizations for SQL Query Execution over Large Language Models | 2025 | SIGMOD |
| 8 | 11,094 | Bridging LLMs and Database Systems: A Deep Dive into Enhanced Relational Operators | 2026 | VLDB |
| 9 | 8,708 | Unstructured Data Analysis Using LLMs: A Comprehensive Benchmark | 2026 | VLDB |
| 10 | 10,231 | Task Cascades for Efficient Unstructured Data Processing | 2026 | SIGMOD |