Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

feat(SQL): incorporate Naru project to SQL #198

Closed
ShannonBase opened this issue Jun 25, 2024 · 0 comments
Closed

feat(SQL): incorporate Naru project to SQL #198

ShannonBase opened this issue Jun 25, 2024 · 0 comments
Assignees
Labels
feature it will be implemented as a new feature

Comments

@ShannonBase
Copy link
Contributor

ShannonBase commented Jun 25, 2024

Summary

Using ML to enhance the table cardinality.

that learns a table’s
data distribution while fully removing heuristic modeling assumptions for the first time, by
applying and enhancing a new statistical model from recent advances in self-supervised learning. Like classical synopses, Naru directly summarizes the data and then uses the summary
to estimate the cardinalities of incoming queries or predicates. Unlike previous estimators,
Naru approximates the joint data distribution of a table without any independence assumptions, thereby achieving a new level of accuracy in base table cardinality estimation.

the Naru poejction at https://github.com/naru-project/naru

ref:
Pandas C++: https://github.com/hosseinmoein/DataFrame

same as #179 , therefore, close this one.

@ShannonBase ShannonBase added the feature it will be implemented as a new feature label Jun 25, 2024
@ShannonBase ShannonBase self-assigned this Jun 25, 2024
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
feature it will be implemented as a new feature
Projects
None yet
Development

No branches or pull requests

1 participant