| 677 |
Hadoop++: Making a Yellow Elephant Run Like a Cheetah (Without It Even Noticing) |
2010 |
VLDB |
39 |
0.00014835154 |
| 1,436 |
Towards a Learning Optimizer for Shared Clouds |
2019 |
VLDB |
45 |
0.00010658836 |
| 1,757 |
Selecting Subexpressions to Materialize at Datacenter Scale |
2018 |
VLDB |
37 |
9.7050769e-05 |
| 1,845 |
Towards a One Size Fits All Database Architecture |
2011 |
CIDR |
22 |
9.507323e-05 |
| 1,940 |
Performance and Resource Modeling in Highly-Concurrent OLTP Workloads |
2013 |
SIGMOD |
26 |
9.3179491e-05 |
| 2,219 |
Magpie: Python at Speed and Scale using Cloud Backends |
2021 |
CIDR |
18 |
8.8186113e-05 |
| 2,360 |
Vertexica: Your Relational Friend for Graph Analytics! |
2014 |
VLDB |
15 |
8.5628814e-05 |
| 2,392 |
The Uncracked Pieces in Database Cracking |
2014 |
VLDB |
22 |
8.5172189e-05 |
| 2,429 |
BigDansing: A System for Big Data Cleansing |
2015 |
SIGMOD |
34 |
8.4657925e-05 |
| 2,828 |
Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings |
2020 |
SIGMOD |
40 |
7.9476509e-05 |
| 3,550 |
Computation Reuse in Analytics Job Service at Microsoft |
2018 |
SIGMOD |
31 |
7.1895215e-05 |
| 3,691 |
Cloudy with High Chance of DBMS: A 10-year Prediction for Enterprise-Grade ML |
2020 |
CIDR |
17 |
7.0771037e-05 |
| 3,951 |
Deploying a Steered Query Optimizer in Production at Microsoft |
2022 |
SIGMOD |
16 |
6.8884038e-05 |
| 4,550 |
AdaptDB: Adaptive Partitioning for Distributed Joins |
2017 |
VLDB |
13 |
6.5327021e-05 |
| 5,126 |
Steering Query Optimizers: A Practical Take on Big Data Workloads |
2021 |
SIGMOD |
22 |
6.2493606e-05 |
| 5,297 |
Only Aggressive Elephants are Fast Elephants |
2012 |
VLDB |
8 |
6.1773472e-05 |
| 6,212 |
AutoExecutor: Predictive Parallelism for Spark SQL Queries |
2021 |
VLDB |
4 |
5.8312703e-05 |
| 6,260 |
The Cosmos Big Data Platform at Microsoft: Over a Decade of Progress and a Decade to Look Forward |
2021 |
VLDB |
10 |
5.815273e-05 |
| 6,921 |
Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation |
2021 |
VLDB |
14 |
5.63068e-05 |
| 7,162 |
Sibyl: Forecasting Time-Evolving Query Workloads |
2024 |
SIGMOD |
6 |
5.5798063e-05 |
| 7,219 |
A Comparison of Knives for Bread Slicing |
2013 |
VLDB |
6 |
5.5673339e-05 |
| 7,377 |
PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! |
2021 |
VLDB |
9 |
5.5238616e-05 |
| 7,764 |
AutoToken: Predicting Peak Parallelism for Big Data Analytics at Microsoft |
2020 |
VLDB |
10 |
5.4403231e-05 |
| 7,830 |
Machine Learning for Cloud Data Systems: the Progress so far and the Path Forward |
2021 |
VLDB |
3 |
5.4294933e-05 |
| 8,362 |
SparkCruise: Workload Optimization in Managed Spark Clusters at Microsoft |
2021 |
VLDB |
5 |
5.3308358e-05 |
| 8,466 |
CARTILAGE: Adding Flexibility to the Hadoop Skeleton |
2013 |
SIGMOD |
3 |
5.3148581e-05 |
| 8,534 |
WWHow! Freeing Data Storage from Cages |
2013 |
CIDR |
5 |
5.3052609e-05 |
| 8,992 |
GEqO: ML-Accelerated Semantic Equivalence Detection |
2023 |
SIGMOD |
2 |
5.2295579e-05 |
| 8,998 |
Pipemizer: An Optimizer for Analytics Data Pipelines |
2022 |
VLDB |
3 |
5.2273528e-05 |
| 9,182 |
Making Data Clouds Smarter at Keebo: Automated Warehouse Optimization using Data Learning |
2023 |
SIGMOD |
3 |
5.1985233e-05 |
| 9,394 |
Phoebe: A Learning-based Checkpoint Optimizer |
2021 |
VLDB |
8 |
5.1676892e-05 |
| 10,034 |
SparkCruise: Handsfree Computation Reuse in Spark |
2019 |
VLDB |
5 |
5.0795508e-05 |
| 10,165 |
Amoeba: A Shape changing Storage System for Big Data |
2016 |
VLDB |
2 |
5.0561788e-05 |
| 11,079 |
Tursio for Credit Unions: Structured Data Search with Automated Context Graphs |
2026 |
VLDB |
0 |
4.9605343e-05 |
| 12,567 |
How Achaeans Would Construct Columns in Troy |
2013 |
CIDR |
2 |
4.9605343e-05 |
| 13,713 |
Turning Databases Into Generative AI Machines |
2024 |
CIDR |
0 |
- |
| 13,758 |
PikePlace: Generating Intelligence for Marketplace Datasets |
2023 |
VLDB |
0 |
- |
| 13,918 |
Robust Data Transformations |
2015 |
CIDR |
0 |
- |