Finding nearest neighbors in high-dimensional spaces is a fundamental operation in many
diverse application domains. Locality Sensitive Hashing (LSH) is one of the most popular …

In malicious URLs detection, traditional classifiers are challenged because the data volume
is huge, patterns are changing over time, and the correlations among features are …

Data similarity (or distance) computation is a fundamental research topic which underpins
many high-level applications based on similarity measures in machine learning and data …

Learning to hash is of particular interest in information retrieval for large-scale data due to its
high efficiency and effectiveness. Most studies in hashing concentrate on constructing new …

There are significant benefits to serve deep learning models from relational databases. First,
features extracted from databases do not need to be transferred to any decoupled deep …

Due to advances in mobile devices and sensors, there has been an increasing interest in
the analysis of multivariate time series. Identifying similar time series is a core subroutine in …

Nearest neighbor (NN) search is inherently computationally expensive in high-dimensional
spaces due to the curse of dimensionality. As a well-known solution, locality-sensitive …

Authorship attribution aims at identifying the original author of an anonymous text from a
given set of candidate authors and has a wide range of applications. The main challenge in …

Min-Hash is a popular technique for efficiently estimating the Jaccard similarity of binary
sets. Consistent Weighted Sampling (CWS) generalizes the Min-Hash scheme to sketch …

Similarity query is the family of queries based on some similarity metrics. Unlike the
traditional database queries which are mostly based on value equality, similarity queries aim …