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 …

In spatial query processing, the popular index R-tree may incur large storage consumption
and high IO cost. Inspired by the recent learned index [17] that replaces B-tree with machine …

Filtering data based on predicates is one of the most fundamental operations for any modern
data warehouse. Techniques to accelerate the execution of filter expressions include …

Learned indexes have been proposed to replace classic index structures like B-Tree with
machine learning (ML) models. They require to replace both the indexes and query …

Machine learning, especially deep learning, is used increasingly to enable better solutions
for data management tasks previously solved by other means, including database indexing …

Spatial relationships are core components in the design and definition of spatial queries. A
spatial relationship determines how two or more spatial objects are related or connected in …

The recently released persistent memory (PM) offers high performance, persistence, and is
cheaper than DRAM. This opens up new possibilities for indexes that operate and persist …

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 …

Recently, Machine Learning (ML, for short) has been successfully applied to database
indexing. Initial experimentation on Learned Indexes has demonstrated better search …

We present the ML-Index, a memory-efficient Multidimensional Learned (ML) structure for
processing point, KNN and range queries. Using data-dependent reference points, the ML …