| 40 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
220 |
0.00046249672 |
| 92 |
CrowdDB: Answering Queries with Crowdsourcing |
2011 |
SIGMOD |
77 |
0.00034613457 |
| 142 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
180 |
0.00029114937 |
| 199 |
CrowdER: Crowdsourcing Entity Resolution |
2012 |
VLDB |
71 |
0.0002551523 |
| 355 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
132 |
0.00020146245 |
| 427 |
ALEX: An Updatable Adaptive Learned Index |
2020 |
SIGMOD |
105 |
0.00018429918 |
| 538 |
MLbase: A Distributed Machine-learning System |
2013 |
CIDR |
34 |
0.00016725463 |
| 550 |
SageDB: A Learned Database System |
2019 |
CIDR |
63 |
0.00016525249 |
| 649 |
Building a Database on S3 |
2008 |
SIGMOD |
34 |
0.00015156241 |
| 697 |
Palimpzest: Optimizing AI-Powered Analytics with Declarative Query Processing |
2025 |
CIDR |
48 |
0.00014665686 |
| 774 |
FITing-Tree: A Data-aware Index Structure |
2019 |
SIGMOD |
70 |
0.00014063511 |
| 834 |
Benchmarking Learned Indexes |
2021 |
VLDB |
61 |
0.00013539098 |
| 870 |
Learning Multi-dimensional Indexes |
2020 |
SIGMOD |
74 |
0.0001332034 |
| 873 |
Leveraging Transitive Relations for Crowdsourced Joins |
2013 |
SIGMOD |
35 |
0.00013306946 |
| 928 |
The End of Slow Networks: It's Time for a Redesign |
2016 |
VLDB |
49 |
0.00013012601 |
| 948 |
Democratizing Data Science through Interactive Curation of ML Pipelines |
2019 |
SIGMOD |
29 |
0.00012900728 |
| 1,023 |
MIRIS: Fast Object Track Queries in Video |
2020 |
SIGMOD |
37 |
0.00012398361 |
| 1,041 |
ARDA: Automatic Relational Data Augmentation for Machine Learning |
2020 |
VLDB |
35 |
0.00012334538 |
| 1,190 |
Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads |
2021 |
VLDB |
61 |
0.00011564169 |
| 1,331 |
An Evaluation of Alternative Architectures for Transaction Processing in the Cloud |
2010 |
SIGMOD |
13 |
0.00010997846 |
| 1,349 |
The End of a Myth: Distributed Transactions Can Scale |
2017 |
VLDB |
46 |
0.00010940559 |
| 1,370 |
Designing Distributed Tree-based Index Structures for Fast RDMA-capable Networks |
2019 |
SIGMOD |
35 |
0.00010875068 |
| 1,416 |
Northstar: An Interactive Data Science System |
2018 |
VLDB |
21 |
0.00010707248 |
| 1,449 |
An Architecture for Compiling UDF-centric Workflows |
2015 |
VLDB |
33 |
0.00010597074 |
| 1,452 |
Consistency Rationing in the Cloud: Pay only when it matters |
2009 |
VLDB |
20 |
0.00010588622 |
| 1,721 |
A Sample-and-Clean Framework for Fast and Accurate Query Processing on Dirty Data |
2014 |
SIGMOD |
38 |
9.7821782e-05 |
| 1,740 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
60 |
9.7289523e-05 |
| 1,791 |
Why TPC Is Not Enough: An Analysis of the Amazon Redshift Fleet |
2024 |
VLDB |
38 |
9.6212919e-05 |
| 1,804 |
IDEBench: A Benchmark for Interactive Data Exploration |
2020 |
SIGMOD |
26 |
9.5971444e-05 |
| 1,812 |
Tuplex: Data Science in Python at Native Code Speed |
2021 |
SIGMOD |
20 |
9.5747668e-05 |
| 1,861 |
CrowdDB: Query Processing with the VLDB Crowd |
2011 |
VLDB |
14 |
9.4694034e-05 |
| 1,887 |
Making the Case for Query-by-Voice with EchoQuery |
2016 |
SIGMOD |
13 |
9.4182022e-05 |
| 1,951 |
S-Store: Streaming Meets Transaction Processing |
2015 |
VLDB |
22 |
9.302451e-05 |
| 2,018 |
PIQL: Success-Tolerant Query Processing in the Cloud |
2012 |
VLDB |
19 |
9.1687512e-05 |
| 2,108 |
Vizdom: Interactive Analytics through Pen and Touch |
2015 |
VLDB |
29 |
9.0229444e-05 |
| 2,139 |
Tupleware: "Big" Data, Big Analytics, Small Clusters |
2015 |
CIDR |
20 |
8.9729383e-05 |
| 2,500 |
A Demonstration of the BigDAWG Polystore System |
2015 |
VLDB |
13 |
8.3598881e-05 |
| 2,772 |
Instance-Optimized Data Layouts for Cloud Analytics Workloads |
2021 |
SIGMOD |
28 |
8.0148928e-05 |
| 2,834 |
DB4ML – An In-Memory Database Kernel with Machine Learning Support |
2020 |
SIGMOD |
13 |
7.9442734e-05 |
| 2,844 |
SNARF: A Learning-Enhanced Range Filter |
2022 |
VLDB |
20 |
7.9291659e-05 |
| 2,848 |
Towards Sustainable Insights or why polygamy is bad for you |
2017 |
CIDR |
6 |
7.9234942e-05 |
| 2,852 |
FactorJoin: A New Cardinality Estimation Framework for Join Queries |
2023 |
SIGMOD |
35 |
7.9201312e-05 |
| 2,878 |
Choosing A Cloud DBMS: Architectures and Tradeoffs |
2019 |
VLDB |
21 |
7.8955094e-05 |
| 2,924 |
Abacus: A Cost-Based Optimizer for Semantic Operator Systems |
2026 |
VLDB |
19 |
7.8407467e-05 |
| 2,995 |
Panda: Performance Debugging for Databases using LLM Agents |
2024 |
CIDR |
13 |
7.7556154e-05 |
| 3,173 |
Rethinking Database High Availability with RDMA Networks |
2019 |
VLDB |
18 |
7.5552708e-05 |
| 3,174 |
S-Store: A Streaming NewSQL System for Big Velocity Applications |
2014 |
VLDB |
14 |
7.552969e-05 |
| 3,207 |
CDFShop: Exploring and Optimizing Learned Index Structures |
2020 |
SIGMOD |
21 |
7.5187675e-05 |
| 3,249 |
TreeLine: An Update-In-Place Key-Value Store for Modern Storage |
2023 |
VLDB |
26 |
7.4761584e-05 |
| 3,269 |
Robust Query Driven Cardinality Estimation under Changing Workloads |
2023 |
VLDB |
38 |
7.463682e-05 |
| 3,293 |
Controlling False Discoveries During Interactive Data Exploration |
2017 |
SIGMOD |
9 |
7.4335786e-05 |
| 3,328 |
Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift |
2023 |
SIGMOD |
27 |
7.4094448e-05 |
| 3,401 |
RTP: Robust Tenant Placement for Elastic In-Memory Database Clusters |
2013 |
SIGMOD |
8 |
7.3225596e-05 |
| 3,428 |
Revisiting Reuse for Approximate Query Processing |
2017 |
VLDB |
22 |
7.2957009e-05 |
| 3,476 |
DBOS: A DBMS-oriented Operating System |
2022 |
VLDB |
16 |
7.2552418e-05 |
| 3,658 |
Davos: A System for Interactive Data-Driven Decision Making |
2021 |
VLDB |
4 |
7.1100991e-05 |
| 3,715 |
The Case for a Learned Sorting Algorithm |
2020 |
SIGMOD |
17 |
7.0549546e-05 |
| 3,724 |
Machine Learning and Databases: The Sound of Things to Come or a Cacophony of Hype? |
2015 |
SIGMOD |
7 |
7.0482307e-05 |
| 3,998 |
Resource Management in Aurora Serverless |
2024 |
VLDB |
11 |
6.8497732e-05 |
| 4,338 |
Generalized Scale Independence Through Incremental Precomputation |
2013 |
SIGMOD |
7 |
6.6413964e-05 |
| 4,343 |
Intelligent Scaling in Amazon Redshift |
2024 |
SIGMOD |
15 |
6.6397106e-05 |
| 4,559 |
Databases Unbound: Querying All of the World’s Bytes with AI |
2024 |
VLDB |
7 |
6.5238675e-05 |
| 4,797 |
LSched: A Workload-Aware Learned Query Scheduler for Analytical Database Systems |
2022 |
SIGMOD |
17 |
6.3978116e-05 |
| 4,833 |
Stage: Query Execution Time Prediction in Amazon Redshift |
2024 |
SIGMOD |
23 |
6.3834856e-05 |
| 4,915 |
Can Learned Models Replace Hash Functions? |
2023 |
VLDB |
8 |
6.3452789e-05 |
| 4,984 |
PrivateClean: Data Cleaning and Differential Privacy |
2016 |
SIGMOD |
4 |
6.3150969e-05 |
| 5,126 |
Steering Query Optimizers: A Practical Take on Big Data Workloads |
2021 |
SIGMOD |
22 |
6.2493606e-05 |
| 5,337 |
Chiller: Contention-centric Transaction Execution and Data Partitioning for Modern Networks |
2020 |
SIGMOD |
16 |
6.1613626e-05 |
| 5,478 |
Crowdsourcing Applications and Platforms: A Data Management Perspective |
2011 |
VLDB |
4 |
6.0999674e-05 |
| 5,627 |
CrowdQ: Crowdsourced Query Understanding |
2013 |
CIDR |
2 |
6.0440058e-05 |
| 5,642 |
Revisiting Reuse in Main Memory Database Systems |
2017 |
SIGMOD |
11 |
6.0370517e-05 |
| 5,935 |
Self-Organizing Data Containers |
2022 |
CIDR |
7 |
5.9251217e-05 |
| 5,982 |
Towards instance-optimized data systems |
2021 |
VLDB |
7 |
5.9096991e-05 |
| 6,041 |
Automated Multidimensional Data Layouts in Amazon Redshift |
2024 |
SIGMOD |
9 |
5.8894534e-05 |
| 6,095 |
From Auto-tuning One Size Fits All to Self-designed and Learned Data-intensive Systems |
2019 |
SIGMOD |
11 |
5.8706416e-05 |
| 6,250 |
Predicate Caching: Query-Driven Secondary Indexing for Cloud Data Warehouses |
2024 |
SIGMOD |
13 |
5.818039e-05 |
| 6,468 |
XQuery in the Browser |
2008 |
SIGMOD |
3 |
5.7579867e-05 |
| 6,526 |
PLANET: Making Progress with Commit Processing in Unpredictable Environments |
2014 |
SIGMOD |
3 |
5.7380126e-05 |
| 6,561 |
Extract-Transform-Load for Video Streams |
2023 |
VLDB |
9 |
5.7288224e-05 |
| 6,881 |
Estimating the Impact of Unknown Unknowns on Aggregate Query Results |
2016 |
SIGMOD |
8 |
5.6415853e-05 |
| 7,363 |
Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD |
2024 |
VLDB |
7 |
5.5279826e-05 |
| 7,621 |
SageDB: An Instance-Optimized Data Analytics System |
2022 |
VLDB |
8 |
5.4664683e-05 |
| 7,740 |
Extending XQuery with Window Functions |
2007 |
VLDB |
5 |
5.447163e-05 |
| 7,776 |
XQuery Reloaded |
2009 |
VLDB |
2 |
5.4386695e-05 |
| 7,827 |
Parachute: Single-Pass Bi-Directional Information Passing |
2025 |
VLDB |
5 |
5.4297836e-05 |
| 8,419 |
The Case for Learned In-Memory Joins |
2023 |
VLDB |
5 |
5.3198816e-05 |
| 8,662 |
PBench: Workload Synthesizer with Real Statistics for Cloud Analytics Benchmarking |
2025 |
VLDB |
2 |
5.2781431e-05 |
| 8,894 |
Stale View Cleaning: Getting Fresh Answers from Stale Materialized Views |
2015 |
VLDB |
10 |
5.2415191e-05 |
| 9,011 |
Improving DBMS Scheduling Decisions with Accurate Performance Prediction on Concurrent Queries |
2025 |
VLDB |
3 |
5.2238303e-05 |
| 9,143 |
Check Out the Big Brain on BRAD: Simplifying Cloud Data Processing with Learned Automated Data Meshes |
2023 |
VLDB |
6 |
5.2074327e-05 |
| 9,420 |
A Data Quality Metric (DQM): How to Estimate the Number of Undetected Errors in Data Sets |
2017 |
VLDB |
2 |
5.1672232e-05 |
| 9,809 |
Cost-based Fault-tolerance for Parallel Data Processing |
2015 |
SIGMOD |
1 |
5.1125251e-05 |
| 9,977 |
Approximate Query Processing for Interactive Data Science |
2017 |
SIGMOD |
1 |
5.0875164e-05 |
| 10,061 |
Tuplex: Robust, Efficient Analytics When Python Rules |
2019 |
VLDB |
1 |
5.0753869e-05 |
| 10,185 |
Deep Research is the New Analytics System: Towards Building the Runtime for AI-Driven Analytics |
2026 |
CIDR |
1 |
5.052396e-05 |
| 10,979 |
Incremental Query Optimizer Statistics in Amazon Redshift |
2026 |
VLDB |
0 |
4.9605343e-05 |
| 11,050 |
Carnot: Interpretable, Interactive, and Optimized Execution of Deep Research Queries |
2026 |
VLDB |
0 |
4.9605343e-05 |
| 11,238 |
Virtualizing Cloud Data Infrastructures with BRAD |
2025 |
SIGMOD |
0 |
4.9605343e-05 |
| 12,338 |
Safe Visual Data Exploration |
2017 |
SIGMOD |
1 |
4.9605343e-05 |
| 12,787 |
Cloudy: A Modular Cloud Storage System |
2010 |
VLDB |
0 |
4.9605343e-05 |
| 13,961 |
Should we all be teaching “Intro to Data Science” instead of “Intro to Databases”? |
2014 |
SIGMOD |
0 |
- |