Neural Relation Understanding: neural cardinality estimators for tabular data
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Updated
Jun 7, 2021 - Python
Neural Relation Understanding: neural cardinality estimators for tabular data
State-of-the-art neural cardinality estimators for join queries
Implementation of DeepDB: Learn from Data, not from Queries!
Fast HyperLogLog for Python.
PilotScope is a middleware to bridge the gaps of deploying AI4DB (Artificial Intelligence for Databases) algorithms into actual database systems.
Union, intersection, and set cardinality in loglog space
Estimating k-mer coverage histogram of genomics data
Dynatrace hash library for Java
A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation
A pytorch implementation for FACE: A Normalizing Flow based Cardinality Estimator
An implementation of the algorithms presented in the paper "Cardinality Estimation Done Right: Index-Based Join Sampling"
Paper about the estimation of cardinalities from HyperLogLog sketches
SetSketch: Filling the Gap between MinHash and HyperLogLog
Paper related to AI4DB techniques
Code for variable skipping ICML 2020 paper
Scalable Join Cardinality Estimaitor
python implementations of the Flajolet-Martin, LogLog, SuperLogLog, and HyperLogLog cardinality estimation algorithms, specifically used to estimate the cardinality of unique traffic violations in NYC in the 2019 fiscal year
Probabilistic data structures (bloom filter / counting bloom filter / linear counter)
Code for Local Deep Learning Models for Cardinality Estimation
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