Nearest neighbor search is a computational primitive whose efficiency is paramount to many
applications. As such, the literature recently blossomed with many works focusing on …

This paper reconsiders common benchmarking approaches to nearest neighbor search. It is
shown that the concepts of local intrinsic dimensionality (LID), local relative contrast (RC) …

Approximate nearest-neighbor search (ANNS) algorithms are a key part of the modern deep
learning stack due to enabling efficient similarity search over high-dimensional vector space …

Mining time series motifs is a fundamental, yet expensive task in exploratory data analytics.
In this paper, we therefore propose a fast method to find the top-k motifs with probabilistic …

We introduce a neural network framework that allows constructing multi-class network traffic
models suitable for flow generation and classification tasks. Packet size and inter-packet …

Abstract Kernel Density Estimation (KDE) is a nonparametric method for estimatig the shape
of a density function, given a set of samples from the distribution. Recently, locality-sensitive …

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 …

The inverted file (IVF) is one of the most efficient non-exhaustive search algorithms for
approximate nearest neighbor (ANN) search. In this paper, we propose a new three-step fast …

We investigate the k-closest pair problem in high dimensions, that is finding the k≥ 1 closest
pairs of points in a set S⊆ X in a metric space (X, dist). This is a fundamental problem in …

Abstract Locality-Sensitive-Hashing (LSH) plays a crucial role in approximate nearest
neighbour search and similarity-based queries. In this paper, we present a study on the …