Text-to-SQL parsing, which aims at converting natural language instructions into executable
SQLs, has gained increasing attention in recent years. In particular, GPT-4 and Claude-2 …

The performance optimization of database systems has been widely studied for years. From
the perspective of the operation and maintenance personnel, it mainly includes three topics …

Database and Artificial Intelligence (AI) can benefit from each other. On one hand, AI can
make database more intelligent (AI4DB). For example, traditional empirical database …

Although learning-based database optimization techniques have been studied from
academia in recent years, they have not been widely deployed in commercial database …

Machine learning has become a prominent method in many database optimization problems
such as cost estimation, index selection and query optimization. Translating query execution …

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 …

Learned databases, or AI4DB techniques, have rapidly developed in the last decade.
Deploying machine learning (ML) and AI4DB algorithms into actual databases is the gold …

Index tuning aims to find the optimal index configuration for an input workload. It is a
resource-intensive task since it requires making multiple expensive" what-if" calls to the …

Machine learning techniques have been proposed to optimize the databases. For example,
traditional empirical database optimization techniques (eg, cost estimation, join order …

Recently, numerous promising results have shown that updatable learned indexes can
perform better than traditional indexes with much lower memory space consumption. But it is …