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 …

Abstract Recently, Charikar and Siminelakis (2017) presented a framework for kernel
density estimation in provably sublinear query time, for kernels that possess a certain …

Approximate similarity queries on high-dimensional vector indices have become the
cornerstone for many critical online services. An increasing need for more sophisticated …

Kernel methods are effective but do not scale well to large scale data, especially in high
dimensions where the geometric data structures used to accelerate kernel evaluation suffer …

In this paper we revisit the kernel density estimation problem: given a kernel K (x, y) and a
dataset of n points in high dimensional Euclidean space, prepare a data structure that can …

In this paper, we investigate the possibilities of utilizing deep learning for cardinality
estimation of similarity selection. Answering this problem accurately and efficiently is …

Kernel density estimation method is a powerful tool and is widely used in many important
real-world applications such as anomaly detection and statistical learning. Unfortunately …

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 …

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 …

Cardinality estimation, which involves estimating the result size of queries, is a critical aspect
of query processing and optimization. Deep Neural Networks (DNNs) are data hungry, and …