Zhengyi Yang

Zhengyi Yang
UNSW Sydney | UNSW · School of Computer Science and Engineering

Doctor of Philosophy

About

23
Publications
1,507
Reads
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129
Citations
Citations since 2017
23 Research Items
129 Citations
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201720182019202020212022202301020304050
201720182019202020212022202301020304050
Introduction
I am an Associate Lecturer in the School of Computer Science and Engineering, University of New South Wales (UNSW). My primary research interests are graph database systems, distributed graph processing and graph mining.

Publications

Publications (23)
Article
Full-text available
Given a user dataset U\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varvec{U}$$\end{document} and an object dataset I\documentclass[12pt]{minimal} \usepackage{amsmat...
Chapter
Structural Graph Clustering (SCAN) is a fundamental problem in graph analysis and has received considerable attention recently. Existing distributed solutions either lack efficiency or suffer from high memory consumption when addressing this problem in billion-scale graphs. Motivated by these, in this paper, we aim to devise a distributed algorithm...
Preprint
Full-text available
Given a user dataset U and an object dataset I , a kNN join query in high-dimensional space returns the k nearest neighbors of each object in dataset U from the object dataset I . The kNN join is a basic and necessary operation in many applications, such as databases, data mining, computer vision, multi-media, machine learning, recommendation syste...
Preprint
Full-text available
Given a user dataset U and an object dataset I, a kNN join query in high-dimensional space returns the k nearest neighbors of each object in dataset U from the object dataset I. The kNN join is a basic and necessary operation in many applications, such as databases, data mining, computer vision, multi-media, machine learning, recommendation systems...
Preprint
Full-text available
Given a user dataset U and an object dataset I, a kNN join query in high-dimensional space returns the k nearest neighbors of each object in dataset U from the object dataset I. The kNN join is a basic and necessary operation in many applications, such as databases, data mining, computer vision, multi-media, machine learning, recommendation systems...
Preprint
Full-text available
Given a user dataset U and an object dataset I, a kNN join query in high-dimensional space returns the k nearest neighbors of each object in dataset U from the object dataset I. The kNN join is a basic and necessary operation in many applications, such as databases, data mining, computer vision, multi-media, machine learning, recommenda-tion system...
Article
Full-text available
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 applications. In mining, specifically, it can be used for the classification of human activities, iterative closest point registration and pattern recognition and has also been he...
Article
Full-text available
Pests and diseases are an inevitable problem in agricultural production, causing substantial economic losses yearly. The application of convolutional neural networks to the intelligent recognition of crop pest images has become increasingly popular due to advances in deep learning methods and the rise of large-scale datasets. However, the diversity...
Chapter
Hop-constrained s-t simple path (\({\textsf{HC}}\text {-}{\textsf{s}}\text {-}{\mathsf {t~path}} \)) enumeration is a fundamental problem in graph analysis. Existing solutions for this problem focus on unlabelled graphs and assume queries are issued without any label constraints. However, in many real-world applications, graphs are edge-labelled an...
Chapter
Given a user dataset U and an object dataset I in high-dimensional space, a kNN join query retrieves each object in dataset U its k nearest neighbors from the dataset I. kNN join is a fundamental and essential operation in applications from many domains such as databases, computer vision, multi-media, machine learning, recommendation systems, and m...
Chapter
Uncertain graphs are graphs where each edge is assigned with a probability of existence. In this paper, we study the problem of hop-constrained s-t simple path enumeration in large uncertain graphs. To the best of our knowledge, we are the first to study this problem in the literature. Specifically, we propose a light-weight index to prune candidat...
Preprint
Full-text available
Subgraph enumeration is a fundamental problem in graph analytics, which aims to find all instances of a given query graph on a large data graph. In this paper, we propose a system called HUGE to efficiently process subgraph enumeration at scale in the distributed context. HUGE features 1) an optimiser to compute an advanced execution plan without t...
Preprint
Full-text available
Subgraph matching is a basic operation widely used in many applications. However, due to its NP-hardness and the explosive growth of graph data, it is challenging to compute subgraph matching, especially in large graphs. In this paper, we aim at scaling up subgraph matching on a single machine using FPGAs. Specifically, we propose a CPU-FPGA co-des...
Chapter
There are many real-world application domains where data can be naturally modelled as a graph, such as social networks and computer networks. Relational Database Management Systems (RDBMS) find it hard to capture the relationships and inherent graph structure of data and are inappropriate for storing highly connected data; thus, graph databases hav...
Chapter
Graphs are widely used to model the intricate relationships among objects in a wide range of applications. The advance in graph data has brought significant value to artificial intelligence technologies. Recently, a number of graph database systems have been developed. In this paper, we present a comprehensive overview and empirical investigation o...
Conference Paper
Graph pattern matching is one of the most fundamental problems in graph database and is associated with a wide spectrum of applications. Due to its computational intensiveness, researchers have primarily devoted their efforts to improving the performance of the algorithm while constraining the graphs to have singular labels on vertices (edges) or n...
Preprint
Full-text available
Recently there emerge many distributed algorithms that aim at solving subgraph matching at scale. Existing algorithm-level comparisons failed to provide a systematic view to the pros and cons of each algorithm mainly due to the intertwining of strategy and optimization. In this paper, we identify four strategies and three general-purpose optimizati...
Article
Full-text available
Recently there emerge many distributed algorithms that aim at solving subgraph matching at scale. Existing algorithm-level comparisons failed to provide a systematic view of distributed subgraph matching mainly due to the intertwining of strategy and optimization. In this paper, we identify four strategies and three general-purpose optimizations fr...

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