Approximate nearest neighbor search (ANNS) constitutes an important operation in a
multitude of applications, including recommendation systems, information retrieval, and …

This paper introduces an efficient and robust framework for hybrid query (HQ) processing,
which combines approximate nearest neighbor search (ANNS) with attribute constraint. HQ …

In modern industrial systems, components have complex interactions with each other, which
makes it become a challenging task to identify the operational conditions of industrial …

By representing the embedded components and their interactions in industrial systems as
nodes and edges in a graph, Graph Neural Networks (GNNs) have achieved outstanding …

As research interest surges, vector similarity search is applied in multiple fields, including
data mining, computer vision, and information retrieval.{Given a set of objects (eg, a set of …

In this paper, we present “FabSearch,” a prototype search engine for sourcing manufacturing
service providers, by making use of the product manufacturing information (PMI) contained …

In many applications of graph processing, the input data is often generated from an
underlying geometric point data set. However, existing high-performance graph processing …

For approximate nearest neighbor search, graph-based algorithms have shown to offer the
best trade-off between accuracy and search time. We propose the Dynamic Exploration …

This survey article considers index structures for fast similarity search for objects
represented by real-valued vectors. Structures for both exact and faster but approximate …

Abstract In recent years, Machine Learning (ML) and Artificial Intelligence (AI) models have
become integral to various business operations, especially within Human Resource (HR) …