| 496 |
Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes |
2024 |
VLDB |
40 |
0.00017318538 |
| 1,245 |
DB-BERT: A Database Tuning Tool that "Reads the Manual" |
2022 |
SIGMOD |
35 |
0.0001136308 |
| 1,605 |
SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning |
2019 |
SIGMOD |
41 |
0.00010095581 |
| 1,800 |
SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning |
2018 |
VLDB |
16 |
9.6082185e-05 |
| 1,975 |
CodexDB: Synthesizing Code for Query Processing from Natural Language Instructions using GPT-3 Codex |
2022 |
VLDB |
15 |
9.2801545e-05 |
| 2,295 |
ThalamusDB: Approximate Query Processing on Multi-Modal Data |
2024 |
SIGMOD |
22 |
8.6822982e-05 |
| 2,524 |
Multi-Objective Parametric Query Optimization |
2015 |
VLDB |
16 |
8.3400278e-05 |
| 2,772 |
Multiple Query Optimization on the D-Wave 2X Adiabatic Quantum Computer |
2016 |
VLDB |
12 |
8.033132e-05 |
| 3,140 |
Solving the Join Ordering Problem via Mixed Integer Linear Programming |
2017 |
SIGMOD |
13 |
7.6012971e-05 |
| 3,648 |
UDO: Universal Database Optimization using Reinforcement Learning |
2021 |
VLDB |
23 |
7.1366536e-05 |
| 4,444 |
From BERT to GPT-3 Codex: Harnessing the Potential of Very Large Language Models for Data Management |
2022 |
VLDB |
10 |
6.5957098e-05 |
| 4,957 |
Verifying Text Summaries of Relational Data Sets |
2019 |
SIGMOD |
9 |
6.3379906e-05 |
| 4,982 |
An Incremental Anytime Algorithm for Multi-Objective Query Optimization |
2015 |
SIGMOD |
10 |
6.3264052e-05 |
| 5,069 |
Scrutinizer: Fact Checking Statistical Claims |
2020 |
VLDB |
6 |
6.28697e-05 |
| 5,200 |
Exact Cardinality Query Optimization with Bounded Execution Cost |
2019 |
SIGMOD |
10 |
6.2303304e-05 |
| 5,227 |
Approximation Schemes for Many-Objective Query Optimization |
2014 |
SIGMOD |
10 |
6.2183387e-05 |
| 5,309 |
SemBench: A Benchmark for Semantic Query Processing Engines |
2026 |
VLDB |
10 |
6.1841176e-05 |
| 5,362 |
Can Large Language Models Predict Data Correlations from Column Names? |
2023 |
VLDB |
4 |
6.1592249e-05 |
| 5,680 |
Mining an "Anti-Knowledge Base" from Wikipedia Updates with Applications to Fact Checking and Beyond |
2020 |
VLDB |
5 |
6.0375644e-05 |
| 5,911 |
Quantum-Inspired Digital Annealing for Join Ordering |
2024 |
VLDB |
6 |
5.9478816e-05 |
| 6,072 |
Demonstrating GPT-DB: Generating Query-Specific and Customizable Code for SQL Processing with GPT-4 |
2023 |
VLDB |
6 |
5.8936535e-05 |
| 6,857 |
SpareLLM: Automatically Selecting Task-Specific Minimum-Cost Large Language Models under Equivalence Constraint |
2025 |
SIGMOD |
4 |
5.6621524e-05 |
| 7,063 |
Towards NLP-Enhanced Data Profiling Tools |
2022 |
CIDR |
3 |
5.6078438e-05 |
| 7,106 |
Data Vocalization with CiceroDB |
2019 |
CIDR |
4 |
5.5991152e-05 |
| 7,205 |
Mining Subjective Properties on the Web |
2015 |
SIGMOD |
4 |
5.5839253e-05 |
| 7,372 |
ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Join Algorithms via Reinforcement Learning |
2023 |
VLDB |
5 |
5.5385404e-05 |
| 7,405 |
Large-Scale Multiple Query Optimisation with Incremental Quantum(-Inspired) Annealing |
2026 |
SIGMOD |
4 |
5.5331324e-05 |
| 7,663 |
Demonstrating the Voice-Based Exploration of Large Data Sets with CiceroDB-Zero |
2020 |
VLDB |
2 |
5.4746904e-05 |
| 7,754 |
AggChecker: A Fact-Checking System for Text Summaries of Relational Data Sets |
2019 |
VLDB |
3 |
5.4574715e-05 |
| 7,773 |
DBG-PT: A Large Language Model Assisted Query Performance Regression Debugger |
2024 |
VLDB |
4 |
5.4539148e-05 |
| 8,021 |
Generating Succinct Descriptions of Database Schemata for Cost-Efficient Prompting of Large Language Models |
2024 |
VLDB |
3 |
5.4041713e-05 |
| 8,043 |
Demonstrating SQLBarber: Leveraging Large Language Models to Generate Customized and Realistic SQL Workloads |
2025 |
SIGMOD |
4 |
5.3999511e-05 |
| 8,238 |
Demonstrating UDO: A Unified Approach for Optimizing Transaction Code, Physical Design, and System Parameters via Reinforcement Learning |
2021 |
SIGMOD |
5 |
5.3696738e-05 |
| 8,264 |
The Case for NLP-Enhanced Database Tuning: Towards Tuning Tools that “Read the Manual” |
2021 |
VLDB |
6 |
5.3648071e-05 |
| 8,531 |
ROME: Robust Query Optimization via Parallel Multi-Plan Execution |
2024 |
SIGMOD |
4 |
5.3206021e-05 |
| 8,659 |
Hybrid Mixed Integer Linear Programming for Large-Scale Join Order Optimisation |
2026 |
VLDB |
2 |
5.292093e-05 |
| 8,847 |
Demonstrating DB-BERT: A Database Tuning Tool that "Reads" the Manual |
2022 |
SIGMOD |
1 |
5.2645421e-05 |
| 8,860 |
SkinnerMT: Parallelizing for Efficiency and Robustness in Adaptive Query Processing on Multicore Platforms |
2023 |
VLDB |
4 |
5.2620362e-05 |
| 8,980 |
A Fast Randomized Algorithm for Multi-Objective Query Optimization |
2016 |
SIGMOD |
1 |
5.2439101e-05 |
| 9,463 |
SQLBarber: A System Leveraging Large Language Models to Generate Customized and Realistic SQL Workloads |
2026 |
SIGMOD |
1 |
5.1712291e-05 |
| 9,628 |
Parallelizing Query Optimization on Shared-Nothing Architectures |
2016 |
VLDB |
6 |
5.1473253e-05 |
| 10,201 |
CEDAR: A System for Cost-Efficient Data-Driven Claim Verification |
2025 |
VLDB |
1 |
5.0599411e-05 |
| 10,982 |
Demonstrating GenDB: Instance-Optimized and Customized Query Processing Code Generation via LLM Agents |
2026 |
VLDB |
0 |
4.9769913e-05 |
| 11,007 |
Demonstrating TACT: Tunable Accuracy-Cost Trade-offs for Semantic Image Filtering |
2026 |
VLDB |
0 |
4.9769913e-05 |
| 11,158 |
Demonstrating CEDAR: A System for Cost-Efficient Data-Driven Claim Verification |
2025 |
SIGMOD |
1 |
4.9769913e-05 |
| 11,174 |
SwellDB: Dynamic Query-Driven Table Generation with Large Language Models |
2025 |
SIGMOD |
0 |
4.9769913e-05 |
| 11,812 |
Demonstrating ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Joins via Reinforcement Learning |
2023 |
VLDB |
1 |
4.9769913e-05 |
| 11,896 |
BABOONS: Black-Box Optimization of Data Summaries in Natural Language |
2022 |
VLDB |
1 |
4.9769913e-05 |
| 11,971 |
Demonstrating Robust Voice Querying with MUVE: Optimally Visualizing Results of Phonetically Similar Queries |
2021 |
SIGMOD |
1 |
4.9769913e-05 |
| 12,014 |
Robust Voice Querying with MUVE: Optimally Visualizing Results of Phonetically Similar Queries |
2021 |
VLDB |
0 |
4.9769913e-05 |
| 12,058 |
BitGourmet: Deterministic Approximation via Optimized Bit Selection |
2020 |
CIDR |
4 |
4.9769913e-05 |
| 12,089 |
Demonstration of BitGourmet: Data Analysis via Deterministic Approximation |
2020 |
SIGMOD |
0 |
4.9769913e-05 |
| 12,123 |
Demonstration of ScroogeDB: Getting More Bang For the Buck with Deterministic Approximation in the Cloud |
2020 |
VLDB |
0 |
4.9769913e-05 |
| 12,148 |
A Holistic Approach for Query Evaluation and Result Vocalization in Voice-Based OLAP |
2019 |
SIGMOD |
5 |
4.9769913e-05 |
| 12,237 |
Vocalizing Large Time Series Efficiently |
2018 |
VLDB |
3 |
4.9769913e-05 |
| 12,306 |
Data Vocalization: Optimizing Voice Output of Relational Data |
2017 |
VLDB |
5 |
4.9769913e-05 |