| 675 |
Hadoop++: Making a Yellow Elephant Run Like a Cheetah (Without It Even Noticing) |
2010 |
VLDB |
39 |
0.00014880686 |
| 1,432 |
Towards a Learning Optimizer for Shared Clouds |
2019 |
VLDB |
45 |
0.00010676754 |
| 1,747 |
Selecting Subexpressions to Materialize at Datacenter Scale |
2018 |
VLDB |
37 |
9.7303647e-05 |
| 1,845 |
Towards a One Size Fits All Database Architecture |
2011 |
CIDR |
22 |
9.5158088e-05 |
| 1,940 |
Performance and Resource Modeling in Highly-Concurrent OLTP Workloads |
2013 |
SIGMOD |
26 |
9.3298436e-05 |
| 2,208 |
Magpie: Python at Speed and Scale using Cloud Backends |
2021 |
CIDR |
18 |
8.8445332e-05 |
| 2,356 |
Vertexica: Your Relational Friend for Graph Analytics! |
2014 |
VLDB |
15 |
8.5852943e-05 |
| 2,384 |
The Uncracked Pieces in Database Cracking |
2014 |
VLDB |
22 |
8.5401256e-05 |
| 2,424 |
BigDansing: A System for Big Data Cleansing |
2015 |
SIGMOD |
34 |
8.483813e-05 |
| 2,833 |
Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings |
2020 |
SIGMOD |
40 |
7.9539771e-05 |
| 3,544 |
Computation Reuse in Analytics Job Service at Microsoft |
2018 |
SIGMOD |
31 |
7.2108612e-05 |
| 3,685 |
Cloudy with High Chance of DBMS: A 10-year Prediction for Enterprise-Grade ML |
2020 |
CIDR |
17 |
7.0972826e-05 |
| 3,948 |
Deploying a Steered Query Optimizer in Production at Microsoft |
2022 |
SIGMOD |
16 |
6.9051584e-05 |
| 4,534 |
AdaptDB: Adaptive Partitioning for Distributed Joins |
2017 |
VLDB |
13 |
6.5535468e-05 |
| 5,110 |
Steering Query Optimizers: A Practical Take on Big Data Workloads |
2021 |
SIGMOD |
22 |
6.2678118e-05 |
| 5,278 |
Only Aggressive Elephants are Fast Elephants |
2012 |
VLDB |
8 |
6.1976381e-05 |
| 6,199 |
AutoExecutor: Predictive Parallelism for Spark SQL Queries |
2021 |
VLDB |
4 |
5.850616e-05 |
| 6,248 |
The Cosmos Big Data Platform at Microsoft: Over a Decade of Progress and a Decade to Look Forward |
2021 |
VLDB |
10 |
5.8345657e-05 |
| 6,903 |
Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation |
2021 |
VLDB |
14 |
5.6493582e-05 |
| 7,160 |
Sibyl: Forecasting Time-Evolving Query Workloads |
2024 |
SIGMOD |
6 |
5.5945786e-05 |
| 7,201 |
A Comparison of Knives for Bread Slicing |
2013 |
VLDB |
6 |
5.5857284e-05 |
| 7,356 |
PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! |
2021 |
VLDB |
9 |
5.5421826e-05 |
| 7,767 |
AutoToken: Predicting Peak Parallelism for Big Data Analytics at Microsoft |
2020 |
VLDB |
10 |
5.4550466e-05 |
| 7,813 |
Machine Learning for Cloud Data Systems: the Progress so far and the Path Forward |
2021 |
VLDB |
3 |
5.4474823e-05 |
| 8,349 |
SparkCruise: Workload Optimization in Managed Spark Clusters at Microsoft |
2021 |
VLDB |
5 |
5.3485189e-05 |
| 8,451 |
CARTILAGE: Adding Flexibility to the Hadoop Skeleton |
2013 |
SIGMOD |
3 |
5.3324907e-05 |
| 8,519 |
WWHow! Freeing Data Storage from Cages |
2013 |
CIDR |
5 |
5.3228159e-05 |
| 8,972 |
GEqO: ML-Accelerated Semantic Equivalence Detection |
2023 |
SIGMOD |
2 |
5.2469075e-05 |
| 8,978 |
Pipemizer: An Optimizer for Analytics Data Pipelines |
2022 |
VLDB |
3 |
5.2446911e-05 |
| 9,169 |
Making Data Clouds Smarter at Keebo: Automated Warehouse Optimization using Data Learning |
2023 |
SIGMOD |
3 |
5.2139049e-05 |
| 9,367 |
Phoebe: A Learning-based Checkpoint Optimizer |
2021 |
VLDB |
8 |
5.1845217e-05 |
| 9,999 |
SparkCruise: Handsfree Computation Reuse in Spark |
2019 |
VLDB |
5 |
5.0964027e-05 |
| 10,128 |
Amoeba: A Shape changing Storage System for Big Data |
2016 |
VLDB |
2 |
5.0729194e-05 |
| 11,018 |
Tursio for Credit Unions: Structured Data Search with Automated Context Graphs |
2026 |
VLDB |
0 |
4.9769913e-05 |
| 12,524 |
How Achaeans Would Construct Columns in Troy |
2013 |
CIDR |
2 |
4.9769913e-05 |
| 13,673 |
Turning Databases Into Generative AI Machines |
2024 |
CIDR |
0 |
- |
| 13,718 |
PikePlace: Generating Intelligence for Marketplace Datasets |
2023 |
VLDB |
0 |
- |
| 13,878 |
Robust Data Transformations |
2015 |
CIDR |
0 |
- |