Federated learning on non-iid data silos: An experimental study
Due to the increasing privacy concerns and data regulations, training data have been
increasingly fragmented, forming distributed databases of multiple “data silos”(eg, within …
increasingly fragmented, forming distributed databases of multiple “data silos”(eg, within …
Learning multi-dimensional indexes
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 …
database engines. To optimize the performance of these operations, databases often create …
RadixSpline: a single-pass learned index
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 …
structures in size and lookup performance. While this is a very promising result, existing …
Benchmarking learned indexes
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 …
structures, like B-Trees, with approximate learned models. In this work, we present a unified …
Tsunami: A learned multi-dimensional index for correlated data and skewed workloads
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 …
data warehouse. Techniques to accelerate the execution of filter expressions include …
AI meets database: AI4DB and DB4AI
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 …
make database more intelligent (AI4DB). For example, traditional empirical database …
Database meets artificial intelligence: A survey
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 …
make database more intelligent (AI4DB). For example, traditional empirical database …
Effectively learning spatial indices
Machine learning, especially deep learning, is used increasingly to enable better solutions
for data management tasks previously solved by other means, including database indexing …
for data management tasks previously solved by other means, including database indexing …
Updatable learned index with precise positions
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 …
processing. The new paradigm of" learned index" has significantly changed the way of …
The internet of federated things (IoFT)
The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the
future, IoFT, the “cloud” will be substituted by the “crowd” where model training is brought to …
future, IoFT, the “cloud” will be substituted by the “crowd” where model training is brought to …