Cardinality estimation (CardEst) plays a significant role in generating high-quality query
plans for a query optimizer in DBMS. In the last decade, an increasing number of advanced …

Knob tuning plays an important role in database optimization, which tunes knob settings to
optimize the database performance or improve resource utilization. However, there are …

Cardinality estimation is one of the most fundamental and challenging problems in query
optimization. Neither classical nor learning-based methods yield satisfactory performance …

Designing effective data manipulation methods is a long standing problem in data lakes.
Traditional methods, which rely on rules or machine learning models, require extensive …

Query plan is widely used as input in machine learning for databases (ML4DB) research,
with query plan representation as a critical step. However, existing studies typically focus on …

For efficient query processing, DBMS query optimizers have for decades relied on delicate
cardinality estimation methods. In this work, we propose an Attention-based LEarned …

Machine Learning (ML) is changing DBs as many DB components are being replaced by ML
models. One open problem in this setting is how to update such ML models in the presence …

Autonomous database management systems (DBMSs) aim to optimize themselves
automatically without human guidance. They rely on machine learning (ML) models that …

In recent times, learned query optimizer has becoming a hot research topic in learned
databases. It serves as the most suitable experimental plots for utilizing numerous machine …

Modern organizations manage their data with a wide variety of specialized cloud database
engines (eg, Aurora, BigQuery, etc.). However, designing and managing such infrastructures …