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 …

Various graphs such as web or social networks may contain up to trillions of edges.
Compressing such datasets can accelerate graph processing by reducing the amount of I/O …

The acquisition, processing, mining, and visualization of sensory data for knowledge
discovery and decision support has recently been a popular area of research and …

The development of information technology has enabled many different technologies to
collect large amounts of spatial data every day. It is of very great significance to discover …

A spatial co-location pattern is a set of spatial features frequently co-occuring in nearby
geographic spaces. Similar to closed frequent itemset mining, closed co-location pattern …

In recent years, learning-based image hashing techniques have elicited wide interest
among researchers because they can be applied in high-dimensional data such as videos …

The size of a streaming graph is possibly unbounded, and it is updated by a continuous
sequence of edges over time. Due to numerous types of real-world interactions, the nature of …

For sample reproduction, texture and color are both significant when the consumer has no
specific or individual demands or cannot describe the requirements clearly. In this paper, an …

Approximate nearest neighbor search (ANN) data structures have widespread applications
in machine learning, computational biology, and text processing. The goal of ANN is to …

Graphs are widely used in various applications, and their size is becoming larger over the
passage of time. It is necessary to reduce their size to minimize main memory needs and to …