Similarity query (aka nearest neighbor query) processing has been an active research topic
for several decades. It is an essential procedure in a wide range of applications (eg …

Quantile Regression (QR) provides a way to approximate a single conditional quantile. To
have a more informative description of the conditional distribution, QR can be merged with …

Cardinality estimation of an approximate substring query is an important problem in
database systems. Traditional approaches build a summary from the text data and estimate …

In this tutorial, we review advances made recently in a decades-old problem, namely query
optimization. Along with the traditional optimization techniques many of which are still being …

Similarity query processing has been an active research topic for several decades. It is an
essential procedure in a wide range of applications. Recently, embedding and auto …

This paper explores the use of machine learning for estimating the selectivity of range
queries in database systems. Using classic learning theory for real-valued functions based …

The emerging deep learning techniques prefer mapping complex data objects (eg, images,
documents) to compact binary vectors (ie, hash codes) for efficient similarity search. In this …

Approximate nearest neighbor (ANN) search in high-dimensional space plays an essential
role in a variety of real-world applications. A well-known solution to ANN search, inverted file …

Approximate nearest neighbor search (ANNS) is a fundamental problem that has attracted
widespread attention for decades. Multi-probe ANNS is one of the most important classes of …

Selectivity estimation aims at estimating the number of database objects that satisfy a
selection criterion. Answering this problem accurately and efficiently is essential to many …