| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059038975 |
| 139 |
Predicate Migration: Optimizing Queries with Expensive Predicates |
1993 |
SIGMOD |
0.00042299329 |
| 340 |
OLTP-Bench: An Extensible Testbed for Benchmarking Relational Databases |
2014 |
VLDB |
0.00026841628 |
| 421 |
Query Optimization in the Presence of Foreign Functions |
1993 |
VLDB |
0.00023711553 |
| 703 |
Query Execution Techniques for Caching Expensive Methods |
1996 |
SIGMOD |
0.00017916705 |
| 853 |
Everything You Always Wanted to Know About Compiled and Vectorized Queries But Were Afraid to Ask |
2018 |
VLDB |
0.00015940507 |
| 934 |
Flexible Database Generators |
2005 |
VLDB |
0.00015227409 |
| 1,108 |
Froid: Optimization of Imperative Programs in a Relational Database |
2018 |
VLDB |
0.00013984276 |
| 1,882 |
Tuplex: Data Science in Python at Native Code Speed |
2021 |
SIGMOD |
0.0001021625 |
| 2,237 |
Procedural Extensions of SQL: Understanding their usage in the wild |
2021 |
VLDB |
9.2212748e-05 |
| 2,418 |
Tupleware: "Big" Data, Big Analytics, Small Clusters |
2015 |
CIDR |
8.8556595e-05 |
| 2,860 |
Optimization of Queries with User-defined Predicates |
1996 |
VLDB |
7.9934503e-05 |
| 2,896 |
Evaluating End-to-End Optimization for Data Analytics Applications in Weld |
2018 |
VLDB |
7.9452051e-05 |
| 2,916 |
Quantifying TPC-H Choke Points and Their Optimizations |
2020 |
VLDB |
7.9068048e-05 |
| 3,080 |
Compiling PL/SQL Away |
2020 |
CIDR |
7.603389e-05 |
| 3,918 |
On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML |
2018 |
VLDB |
6.6315176e-05 |
| 4,813 |
Putting Pandas in a Box |
2021 |
CIDR |
5.9049746e-05 |
| 4,924 |
User-Defined Operators: Efficiently Integrating Custom Algorithms into Modern Databases |
2022 |
VLDB |
5.822682e-05 |
| 5,723 |
Evolution of a Compiling Query Engine |
2021 |
VLDB |
5.3522361e-05 |
| 5,731 |
Babelfish: Efficient Execution of Polyglot Queries |
2022 |
VLDB |
5.3502065e-05 |
| 6,189 |
Accelerating Python UDFs in Vectorized Query Execution |
2022 |
CIDR |
5.1647573e-05 |
| 6,375 |
Dear User-Defined Functions, Inlining isn't working out so great for us. Let's try batching to make our relationship work. Sincerely, SQL |
2024 |
CIDR |
5.0923872e-05 |
| 6,645 |
Functional-Style SQL UDFs With a Capital 'F' |
2020 |
SIGMOD |
4.978205e-05 |
| 6,701 |
YeSQL: “You extend SQL” with Rich and Highly Performant User-Defined Functions in Relational Databases |
2022 |
VLDB |
4.9561066e-05 |
| 6,863 |
Declarative Sub-Operators for Universal Data Processing |
2023 |
VLDB |
4.905092e-05 |
| 7,059 |
Adaptive and Robust Query Execution for Lakehouses at Scale |
2024 |
VLDB |
4.8477825e-05 |
| 7,338 |
Aero: Adaptive Query Processing of ML Queries |
2025 |
SIGMOD |
4.7584583e-05 |
| 8,479 |
Excalibur: A Virtual Machine for Adaptive Fine-grained JIT-Compiled Query Execution based on VOILA |
2023 |
VLDB |
4.5014929e-05 |
| 8,583 |
Efficient Execution of User-Defined Functions in SQL Queries |
2023 |
VLDB |
4.4919445e-05 |
| 8,645 |
Predicate Pushdown for Data Science Pipelines |
2023 |
SIGMOD |
4.4772518e-05 |
| 9,343 |
The Key to Effective UDF Optimization: Before Inlining, First Perform Outlining |
2025 |
VLDB |
4.3546206e-05 |
| 9,883 |
Towards Resource-adaptive Query Execution in Cloud Native Databases |
2024 |
CIDR |
4.2635782e-05 |
| 9,884 |
SQL Engines Excel at the Execution of Imperative Programs |
2024 |
VLDB |
4.2635782e-05 |
| 13,400 |
BabbleFlow - A Translator for Analytic Data Flow Programs |
2014 |
SIGMOD |
- |