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Database Information Systems

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Database Information Systems

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Research Frontiers in Machine Learning-Based Query Cardinality Estimation

Development of neural network and machine learning approaches to predict query result sizes for improved query optimization and resource allocation.

Learned Index Structures for Cardinality Prediction
Neural Selectivity Modeling in Heterogeneous Data Distributions
Multi-Modal Learning for Complex Join Estimation
Adaptive Cardinality Models Across Query Workloads
Deep Learning on Relational Schema Structure
Uncertainty Quantification in Neural Estimators
Transfer Learning Across Database Systems
Graph Neural Networks for Query Dependency Learning
Cardinality Estimation Under Data Distribution Drift
Learned Cost Models for Selective Materialization

All Database & Information Systems PhD categories