Pinned Repositories
AiOceanbase
are-u-ok
你还好🐎
CE-baselines
测试作为baselines的SOTAs的性能
cold-hot-data-leveldb
deepdb-public
Implementation of DeepDB: Learn from Data, not from Queries!
Efficient-VDVAE-DB
test the Official Pytorch implementation of "Efficient-VDVAE: Less is more"
FACE-A-Normalizing-Flow-based-Cardinality-Estimator
A pytorch implementation for FACE: A Normalizing Flow based Cardinality Estimator
FactorJoin
A new cardinality estimation scheme for join query estimation
hello-world
学习使用github
learning_cardinality_estimator_distributed_exploration
在分布式条件下评估已有的学习型基数估计器性能，研究探索适用于分布式数据库的学习型基数估计器应具有的性质和特点
spiceandwolf's Repositories
spiceandwolf/cold-hot-data-leveldb
spiceandwolf/learning_cardinality_estimator_distributed_exploration
在分布式条件下评估已有的学习型基数估计器性能，研究探索适用于分布式数据库的学习型基数估计器应具有的性质和特点
spiceandwolf/AiOceanbase
spiceandwolf/are-u-ok
你还好🐎
spiceandwolf/CE-baselines
测试作为baselines的SOTAs的性能
spiceandwolf/deepdb-public
Implementation of DeepDB: Learn from Data, not from Queries!
spiceandwolf/Efficient-VDVAE-DB
test the Official Pytorch implementation of "Efficient-VDVAE: Less is more"
spiceandwolf/FACE-A-Normalizing-Flow-based-Cardinality-Estimator
A pytorch implementation for FACE: A Normalizing Flow based Cardinality Estimator
spiceandwolf/FactorJoin
A new cardinality estimation scheme for join query estimation
spiceandwolf/hello-world
学习使用github
spiceandwolf/learnedcardinalities
Code and workloads from the Learned Cardinalities paper (https://arxiv.org/abs/1809.00677)
spiceandwolf/learngit
spiceandwolf/naru
Neural Relation Understanding: neural cardinality estimators for tabular data
spiceandwolf/neurocard
State-of-the-art neural cardinality estimators for join queries
spiceandwolf/NNGP-src
NNGP Estimator of [SIGMOD'22] Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process
spiceandwolf/PRICE
A Pretrained Model for Cross-Database Cardinality Estimation
spiceandwolf/tensorflow_cpp_test
spiceandwolf/vdvae-db
利用vdvae建模数据库schema. refer to the paper "Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images"