Due to the increasing privacy concerns and data regulations, training data have been
increasingly fragmented, forming distributed databases of multiple “data silos”(eg, within …

Scanning and filtering over multi-dimensional tables are key operations in modern analytical
database engines. To optimize the performance of these operations, databases often create …

Recent research has shown that learned models can outperform state-of-the-art index
structures in size and lookup performance. While this is a very promising result, existing …

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 …

Recent advancements in learned index structures propose replacing existing index
structures, like B-Trees, with approximate learned models. In this work, we present a unified …

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 …

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 …

Indexes can improve query-processing performance by avoiding full table scans. Although
traditional indexes (eg, B+-tree) have been widely used, learned indexes are proposed to …

Index plays an essential role in modern database engines to accelerate the query
processing. The new paradigm of" learned index" has significantly changed the way of …

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 …