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Found 10 papers, 6 papers with code
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation
Despite decades of research, existing methods either over simplify the models only using independent factorization which leads to inaccurate estimates, or over complicate them by lossless conditional factorization without any independent assumption which results in slow probability computation.
BayesCard: Revitilizing Bayesian Frameworks for Cardinality Estimation
Recently proposed deep learning based methods largely improve the estimation accuracy but their performance can be greatly affected by data and often difficult for system deployment.
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation
Therefore, we propose a new metric P-Error to evaluate the performance of CardEst methods, which overcomes the limitation of Q-Error and is able to reflect the overall end-to-end performance of CardEst methods.
Lero: A Learning-to-Rank Query Optimizer
In this paper, we introduce a learning-to-rank query optimizer, called Lero, which builds on top of a native query optimizer and continuously learns to improve the optimization performance.
A Unified Transferable Model for ML-Enhanced DBMS
We propose to explore the transferabilities of the ML methods both across tasks and across DBs to tackle these fundamental drawbacks.
Extract-Transform-Load for Video Streams
We find that no current system sufficiently fulfills both needs and therefore propose Skyscraper, a system tailored to V-ETL.
FSPN: A New Class of Probabilistic Graphical Model
We introduce factorize sum split product networks (FSPNs), a new class of probabilistic graphical models (PGMs).
Complexity and Diversity in Sparse Code Priors Improve Receptive Field Characterization of Macaque V1 Neurons
System identification techniques -- projection pursuit regression models (PPRs) and convolutional neural networks (CNNs) -- provide state-of-the-art performance in predicting visual cortical neurons' responses to arbitrary input stimuli.
Efficient and Scalable Structure Learning for Bayesian Networks: Algorithms and Applications
To resolve this, we propose a new structure learning algorithm LEAST, which comprehensively fulfills our business requirements as it attains high accuracy, efficiency and scalability at the same time.
FactorJoin: A New Cardinality Estimation Framework for Join Queries
Neither classical nor learning-based methods yield satisfactory performance when estimating the cardinality of the join queries.
