| 494 |
Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes |
2024 |
VLDB |
40 |
0.00017350554 |
| 1,238 |
DB-BERT: A Database Tuning Tool that "Reads the Manual" |
2022 |
SIGMOD |
35 |
0.00011375179 |
| 1,609 |
SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning |
2019 |
SIGMOD |
41 |
0.0001007196 |
| 1,803 |
SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning |
2018 |
VLDB |
16 |
9.5987229e-05 |
| 1,931 |
CodexDB: Synthesizing Code for Query Processing from Natural Language Instructions using GPT-3 Codex |
2022 |
VLDB |
15 |
9.3449634e-05 |
| 2,225 |
ThalamusDB: Approximate Query Processing on Multi-Modal Data |
2024 |
SIGMOD |
23 |
8.7996555e-05 |
| 2,531 |
Multi-Objective Parametric Query Optimization |
2015 |
VLDB |
16 |
8.3140235e-05 |
| 2,781 |
Multiple Query Optimization on the D-Wave 2X Adiabatic Quantum Computer |
2016 |
VLDB |
12 |
8.0065695e-05 |
| 3,144 |
Solving the Join Ordering Problem via Mixed Integer Linear Programming |
2017 |
SIGMOD |
13 |
7.5846375e-05 |
| 3,655 |
UDO: Universal Database Optimization using Reinforcement Learning |
2021 |
VLDB |
23 |
7.1137504e-05 |
| 4,464 |
From BERT to GPT-3 Codex: Harnessing the Potential of Very Large Language Models for Data Management |
2022 |
VLDB |
10 |
6.5747093e-05 |
| 4,904 |
Verifying Text Summaries of Relational Data Sets |
2019 |
SIGMOD |
9 |
6.3477561e-05 |
| 4,996 |
SemBench: A Benchmark for Semantic Query Processing Engines |
2026 |
VLDB |
11 |
6.3095672e-05 |
| 5,006 |
An Incremental Anytime Algorithm for Multi-Objective Query Optimization |
2015 |
SIGMOD |
10 |
6.3057396e-05 |
| 5,088 |
Scrutinizer: Fact Checking Statistical Claims |
2020 |
VLDB |
6 |
6.2661814e-05 |
| 5,223 |
Exact Cardinality Query Optimization with Bounded Execution Cost |
2019 |
SIGMOD |
10 |
6.210331e-05 |
| 5,248 |
Approximation Schemes for Many-Objective Query Optimization |
2014 |
SIGMOD |
10 |
6.1980997e-05 |
| 5,377 |
Can Large Language Models Predict Data Correlations from Column Names? |
2023 |
VLDB |
4 |
6.1396287e-05 |
| 5,694 |
Mining an "Anti-Knowledge Base" from Wikipedia Updates with Applications to Fact Checking and Beyond |
2020 |
VLDB |
5 |
6.0176005e-05 |
| 5,926 |
Quantum-Inspired Digital Annealing for Join Ordering |
2024 |
VLDB |
6 |
5.9282142e-05 |
| 6,085 |
Demonstrating GPT-DB: Generating Query-Specific and Customizable Code for SQL Processing with GPT-4 |
2023 |
VLDB |
6 |
5.8745517e-05 |
| 6,874 |
SpareLLM: Automatically Selecting Task-Specific Minimum-Cost Large Language Models under Equivalence Constraint |
2025 |
SIGMOD |
4 |
5.6434298e-05 |
| 7,060 |
Towards NLP-Enhanced Data Profiling Tools |
2022 |
CIDR |
3 |
5.5941856e-05 |
| 7,129 |
Data Vocalization with CiceroDB |
2019 |
CIDR |
4 |
5.580601e-05 |
| 7,223 |
Mining Subjective Properties on the Web |
2015 |
SIGMOD |
4 |
5.5654701e-05 |
| 7,394 |
ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Join Algorithms via Reinforcement Learning |
2023 |
VLDB |
5 |
5.5202266e-05 |
| 7,423 |
Large-Scale Multiple Query Optimisation with Incremental Quantum(-Inspired) Annealing |
2026 |
SIGMOD |
4 |
5.5148364e-05 |
| 7,676 |
Demonstrating the Voice-Based Exploration of Large Data Sets with CiceroDB-Zero |
2020 |
VLDB |
2 |
5.4565877e-05 |
| 7,767 |
AggChecker: A Fact-Checking System for Text Summaries of Relational Data Sets |
2019 |
VLDB |
3 |
5.4394257e-05 |
| 7,768 |
DBG-PT: A Large Language Model Assisted Query Performance Regression Debugger |
2024 |
VLDB |
4 |
5.4392507e-05 |
| 8,037 |
Generating Succinct Descriptions of Database Schemata for Cost-Efficient Prompting of Large Language Models |
2024 |
VLDB |
3 |
5.3863017e-05 |
| 8,061 |
Demonstrating SQLBarber: Leveraging Large Language Models to Generate Customized and Realistic SQL Workloads |
2025 |
SIGMOD |
4 |
5.3820955e-05 |
| 8,253 |
Demonstrating UDO: A Unified Approach for Optimizing Transaction Code, Physical Design, and System Parameters via Reinforcement Learning |
2021 |
SIGMOD |
5 |
5.353686e-05 |
| 8,274 |
The Case for NLP-Enhanced Database Tuning: Towards Tuning Tools that “Read the Manual” |
2021 |
VLDB |
6 |
5.3477381e-05 |
| 8,550 |
ROME: Robust Query Optimization via Parallel Multi-Plan Execution |
2024 |
SIGMOD |
4 |
5.3030089e-05 |
| 8,677 |
Hybrid Mixed Integer Linear Programming for Large-Scale Join Order Optimisation |
2026 |
VLDB |
2 |
5.2745941e-05 |
| 8,866 |
Demonstrating DB-BERT: A Database Tuning Tool that "Reads" the Manual |
2022 |
SIGMOD |
1 |
5.2471343e-05 |
| 8,872 |
SkinnerMT: Parallelizing for Efficiency and Robustness in Adaptive Query Processing on Multicore Platforms |
2023 |
VLDB |
4 |
5.2459616e-05 |
| 8,999 |
A Fast Randomized Algorithm for Multi-Objective Query Optimization |
2016 |
SIGMOD |
1 |
5.2270052e-05 |
| 9,494 |
SQLBarber: A System Leveraging Large Language Models to Generate Customized and Realistic SQL Workloads |
2026 |
SIGMOD |
1 |
5.1541298e-05 |
| 9,656 |
Parallelizing Query Optimization on Shared-Nothing Architectures |
2016 |
VLDB |
6 |
5.1303051e-05 |
| 10,234 |
CEDAR: A System for Cost-Efficient Data-Driven Claim Verification |
2025 |
VLDB |
1 |
5.0432098e-05 |
| 11,043 |
Demonstrating GenDB: Instance-Optimized and Customized Query Processing Code Generation via LLM Agents |
2026 |
VLDB |
0 |
4.9605343e-05 |
| 11,068 |
Demonstrating TACT: Tunable Accuracy-Cost Trade-offs for Semantic Image Filtering |
2026 |
VLDB |
0 |
4.9605343e-05 |
| 11,217 |
Demonstrating CEDAR: A System for Cost-Efficient Data-Driven Claim Verification |
2025 |
SIGMOD |
1 |
4.9605343e-05 |
| 11,233 |
SwellDB: Dynamic Query-Driven Table Generation with Large Language Models |
2025 |
SIGMOD |
0 |
4.9605343e-05 |
| 11,856 |
Demonstrating ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Joins via Reinforcement Learning |
2023 |
VLDB |
1 |
4.9605343e-05 |
| 11,940 |
BABOONS: Black-Box Optimization of Data Summaries in Natural Language |
2022 |
VLDB |
1 |
4.9605343e-05 |
| 12,015 |
Demonstrating Robust Voice Querying with MUVE: Optimally Visualizing Results of Phonetically Similar Queries |
2021 |
SIGMOD |
1 |
4.9605343e-05 |
| 12,058 |
Robust Voice Querying with MUVE: Optimally Visualizing Results of Phonetically Similar Queries |
2021 |
VLDB |
0 |
4.9605343e-05 |
| 12,102 |
BitGourmet: Deterministic Approximation via Optimized Bit Selection |
2020 |
CIDR |
4 |
4.9605343e-05 |
| 12,133 |
Demonstration of BitGourmet: Data Analysis via Deterministic Approximation |
2020 |
SIGMOD |
0 |
4.9605343e-05 |
| 12,167 |
Demonstration of ScroogeDB: Getting More Bang For the Buck with Deterministic Approximation in the Cloud |
2020 |
VLDB |
0 |
4.9605343e-05 |
| 12,191 |
A Holistic Approach for Query Evaluation and Result Vocalization in Voice-Based OLAP |
2019 |
SIGMOD |
5 |
4.9605343e-05 |
| 12,280 |
Vocalizing Large Time Series Efficiently |
2018 |
VLDB |
3 |
4.9605343e-05 |
| 12,349 |
Data Vocalization: Optimizing Voice Output of Relational Data |
2017 |
VLDB |
5 |
4.9605343e-05 |