Deep Learning (DL) has had an immense success in the recent past, leading to state-of-the-
art results in various domains, such as image recognition and natural language processing …

While FPGAs have seen prior use in database systems, in recent years interest in using
FPGA to accelerate databases has declined in both industry and academia for the following …

Entity matching (EM) finds data instances that refer to the same real-world entity. In this
paper we examine applying deep learning (DL) to EM, to understand DL's benefits and …

Query optimization is one of the most challenging problems in database systems. Despite
the progress made over the past decades, query optimizers remain extremely complex …

Configuration tuning is vital to optimize the performance of database management system
(DBMS). It becomes more tedious and urgent for cloud databases (CDB) due to the diverse …

Distributed deep neural network training (DT) systems are widely deployed in clusters where
the network is shared across multiple tenants, ie, multiple DT jobs. Each DT job computes …

Surprisingly promising results have been achieved by deep learning (DL) systems in recent
years. Many of these achievements have been reached in academic settings, or by large …

Abstract Machine learning has recently emerged as a powerful technique to increase
operational efficiency or to develop new value propositions. However, the translation of a …

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 …

Deep learning (DL) based software systems are difficult to develop and maintain in industrial
settings due to several challenges. Data management is one of the most prominent …