In almost every scientific field, measurements are performed over time. These observations
lead to a collection of organized data called time series. The purpose of time-series data …

k nearest neighbours (kNN) queries are fundamental in many applications, ranging from
data mining, recommendation system and Internet of Things, to Industry 4.0 framework …

Similarity indices for high-dimensional data are very desirable for building content-based
search systems for featurerich data such as audio, images, videos, and other sensor data …

The skyline of ad-dimensional dataset contains the points that are not dominated by any
other point on all dimensions. Skyline computation has recently received considerable …

In this article, we present an efficient B+-tree based indexing method, called iDistance, for K-
nearest neighbor (KNN) search in a high-dimensional metric space. iDistance partitions the …

Modern On-Line Data Intensive (OLDI) applications have evolved from monolithic systems to
instead comprise numerous, distributed microservices interacting via Remote Procedure …

Given two sets of points P and Q, a group nearest neighbor (GNN) query retrieves the point
(s) of P with the smallest sum of distances to all points in Q. Consider, for instance, three …

In this paper, we present an efficient method, called iDistance, for K-nearest neighbor (KNN)
search in a high-dimensional space. iDistance partitions the data and selects a reference …

Given two spatial datasets P (eg, facilities) and Q (queries), an aggregate nearest neighbor
(ANN) query retrieves the point (s) of P with the smallest aggregate distance (s) to points in …

Spatial queries in high-dimensional spaces have been studied extensively. Among them,
nearest neighbor queries are important in many settings, including spatial databases (Find …