Abstract The k-Nearest Neighbors (kNN) method, established in 1951, has since evolved
into a pivotal tool in data mining, recommendation systems, and Internet of Things (IoT) …

Rapid developments in hardware, software, and communication technologies have
facilitated the emergence of Internet-connected sensory devices that provide observations …

K nearest neighbors (kNN) is an efficient lazy learning algorithm and has successfully been
developed in real applications. It is natural to scale the kNN method to the large scale …

Approximate nearest neighbor search (ANNS) is a fundamental problem in databases and
data mining. A scalable ANNS algorithm should be both memory-efficient and fast. Some …

Computed tomography image (CTI) sequence is essentially a time‐series data that typically
consists of a large amount of nearby and similar CTIs. Due to the high communication and …

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 …

Filtering data based on predicates is one of the most fundamental operations for any modern
data warehouse. Techniques to accelerate the execution of filter expressions include …

In this paper, we present a new multimedia retrieval paradigm to innovate large-scale
search of heterogenous multimedia data. It is able to return results of different media types …

Finding useful patterns in large datasets has attracted considerable interest recently, and
one of the most widely studied problems in this area is the identification of clusters, or …

Object goal navigation (ObjectNav) in unseen environments is a fundamental task for
Embodied AI. Agents in existing works learn ObjectNav policies based on 2D maps, scene …