A recent line of works apply machine learning techniques to assist or rebuild cost-based
query optimizers in DBMS. While exhibiting superiority in some benchmarks, their …

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

Data lakes are becoming increasingly prevalent for Big Data management and data
analytics. In contrast to traditional 'schema-on-write'approaches such as data warehouses …

In this paper, we introduce zero-shot cost models which enable learned cost estimation that
generalizes to unseen databases. In contrast to state-of-the-art workload-driven approaches …

Query optimization is a key component in database management systems (DBMS) and
distributed data processing platforms. Recent research in the database community …

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 …

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

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 …

Machine learning models based on neural networks (NNs) are enjoying ever-increasing
attention in the Database (DB) community, both in research and practice. However, an …

Learned database systems address several weaknesses of traditional cost estimation
techniques in query optimization: they learn a model of a database instance, eg, as queries …