Beyond Query: Exploratory Subgraph Discovery and Search System. Exploring co-working user groups in dynamic network data is a vital challenge in many applications, for example, in online education. This project aims to discover new relationships of users and compute their co-working performance in continuous time periods. The outcomes of the project are to design effective subgraph exploratory models, three novel types of subgraph search solutions, and devise a friendly exploratory subgraph sear ....Beyond Query: Exploratory Subgraph Discovery and Search System. Exploring co-working user groups in dynamic network data is a vital challenge in many applications, for example, in online education. This project aims to discover new relationships of users and compute their co-working performance in continuous time periods. The outcomes of the project are to design effective subgraph exploratory models, three novel types of subgraph search solutions, and devise a friendly exploratory subgraph search system for supporting the real-time network data analytics. The success of the project will make a significant contribution to the scientific foundation of graph data mining and its applications in data engineering domains, as well as benefiting co-working performance of people in Australian labor markets.Read moreRead less
Small Scalable Natural Language Models using Explicit Memory. Deep neural networks have had spectacular success in natural language processing, seeing wide-spread deployment as part of automatic assistant devices in homes and cars, and across many valuable industries including finance, medicine and law. Fueling this success is the use of ever larger models, with exponentially increasing training resources, accompanying hardware and energy demands. This project aims to develop more compact models ....Small Scalable Natural Language Models using Explicit Memory. Deep neural networks have had spectacular success in natural language processing, seeing wide-spread deployment as part of automatic assistant devices in homes and cars, and across many valuable industries including finance, medicine and law. Fueling this success is the use of ever larger models, with exponentially increasing training resources, accompanying hardware and energy demands. This project aims to develop more compact models, based on the incorporation of an explicit searchable memory, which will dramatically reduce model size, hardware requirements and energy usage. This will make modern natural language processing more accessible, while also providing greater flexibility, allowing for more adaptable and portable technologies.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100200
Funder
Australian Research Council
Funding Amount
$428,847.00
Summary
Cohesive Multipartite Subgraph Discovery in Large Heterogeneous Networks. This project aims to devise novel cohesive multipartite subgraph models and corresponding efficient search algorithms based on various applications. Significant advances in understanding big data will be enabled by the proposed novel theories and algorithms, which can leverage the value of heterogeneous network data and serve as the foundation of network analytics. Expected outcomes of this project include novel cohesive m ....Cohesive Multipartite Subgraph Discovery in Large Heterogeneous Networks. This project aims to devise novel cohesive multipartite subgraph models and corresponding efficient search algorithms based on various applications. Significant advances in understanding big data will be enabled by the proposed novel theories and algorithms, which can leverage the value of heterogeneous network data and serve as the foundation of network analytics. Expected outcomes of this project include novel cohesive multipartite subgraph models, efficient searching algorithms and platforms for heterogeneous networks. This should provide significant benefits for different organisations and a myriad of applications dealing with heterogeneous network data, including but not limited to e-commerce, cybersecurity, health and social networks.Read moreRead less
Big temporal graph processing in the Cloud. This project aims to develop efficient and scalable algorithms to process big temporal graphs in the Cloud. In particular, we will investigate three most representative types of queries over big temporal graphs including vertex-based queries, path-based queries, and subgraph-based queries. Expected outcomes of this project include theoretical foundations and scalable algorithms to process big temporal graphs as well as a system prototype for evaluation ....Big temporal graph processing in the Cloud. This project aims to develop efficient and scalable algorithms to process big temporal graphs in the Cloud. In particular, we will investigate three most representative types of queries over big temporal graphs including vertex-based queries, path-based queries, and subgraph-based queries. Expected outcomes of this project include theoretical foundations and scalable algorithms to process big temporal graphs as well as a system prototype for evaluation and to demonstrate the practical value. Success in this project should see significant benefits for many important applications such as cybersecurity, e-commerce, health and road networks.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100668
Funder
Australian Research Council
Funding Amount
$435,000.00
Summary
Towards Processing of Big Streaming Temporal Graphs. This project aims to develop efficient and scalable algorithms to process big streaming temporal graphs, which is in high demand for many data-intensive applications such as cybersecurity, crime monitoring, and e-marketing. In particular, I will investigate three most representative types of queries including vertex-based queries, path-based queries, and subgraph-based queries. Expected outcomes of this project include theoretical foundations ....Towards Processing of Big Streaming Temporal Graphs. This project aims to develop efficient and scalable algorithms to process big streaming temporal graphs, which is in high demand for many data-intensive applications such as cybersecurity, crime monitoring, and e-marketing. In particular, I will investigate three most representative types of queries including vertex-based queries, path-based queries, and subgraph-based queries. Expected outcomes of this project include theoretical foundations and scalable algorithms to process big streaming temporal graphs as well as a system prototype for evaluation and to demonstrate the practical value. Success in this project should see significant benefits for many important applications such as cybersecurity, e-commerce, health and social analysis.Read moreRead less
Next-Generation Distributed Graph Engine for Big Graphs. This project aims to develop an efficient and scalable distributed graph engine to process big graphs. In particular, we will investigate the foundations for the distributed real-time graph engine, focusing on graph storage and graph operators, and then provide solutions for a set of representative graph mining and query processing tasks. Expected outcomes of this project include theoretical foundations and a scalable real-time graph engin ....Next-Generation Distributed Graph Engine for Big Graphs. This project aims to develop an efficient and scalable distributed graph engine to process big graphs. In particular, we will investigate the foundations for the distributed real-time graph engine, focusing on graph storage and graph operators, and then provide solutions for a set of representative graph mining and query processing tasks. Expected outcomes of this project include theoretical foundations and a scalable real-time graph engine to process big graphs as well as a system prototype for evaluation and to demonstrate the practical value. Success in this project should see significant benefits for many important applications such as cybersecurity, e-commerce, health and road networks.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100165
Funder
Australian Research Council
Funding Amount
$443,847.00
Summary
Evolving privacy and utility in data storage and publishing. This project aims to develop a distributed evolutionary computation-based framework to optimize data privacy and utility in distributed database systems. It intends to synchronously solve the conflicting challenges of privacy preservation and utility maintenance in multi-objective, dynamic, and multitasking scenarios. Expected outcomes include a new computation framework as a service and freely available distributed computation models, ....Evolving privacy and utility in data storage and publishing. This project aims to develop a distributed evolutionary computation-based framework to optimize data privacy and utility in distributed database systems. It intends to synchronously solve the conflicting challenges of privacy preservation and utility maintenance in multi-objective, dynamic, and multitasking scenarios. Expected outcomes include a new computation framework as a service and freely available distributed computation models, evolutionary algorithms, and knowledge-transfer strategies. Anticipated benefits include theoretical contributions to artificial intelligence, cyber security, distributed computation, and a service to eliminate data owners’ privacy concerns while guaranteeing the value of data in further utilization.Read moreRead less
Improved security and privacy for online platforms. Online platforms provide goods and services to people all over the world in a flexible way. Due to COVID-19, the number of online platforms increased significantly. As more and more business activities are conducted in a virtual environment, there is a corresponding increase in major privacy and security challenges. This project aims to work in the online education industry to provide a revolutionary secure environment for both business owners ....Improved security and privacy for online platforms. Online platforms provide goods and services to people all over the world in a flexible way. Due to COVID-19, the number of online platforms increased significantly. As more and more business activities are conducted in a virtual environment, there is a corresponding increase in major privacy and security challenges. This project aims to work in the online education industry to provide a revolutionary secure environment for both business owners and users. This secure online environment will enable privacy and security guarantees that will be first implemented on our Partner Organisation’s education platform. The developed technologies can be easily adapted to most online-service industries and can be commercialised immediately.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240101364
Funder
Australian Research Council
Funding Amount
$445,591.00
Summary
Governing Industrial Data Ecosystems: Open Innovation in a Digital Economy. This project aims to investigate how governance mechanisms incentivise multilateral data-sharing to enable open innovation in industrial data ecosystems. Based on a rigorous multi-method study at ecosystem, firm and managerial levels, a framework of generative open innovation to govern multilateral data sharing will be developed. By addressing data-sharing barriers at all levels, the framework helps create collective val ....Governing Industrial Data Ecosystems: Open Innovation in a Digital Economy. This project aims to investigate how governance mechanisms incentivise multilateral data-sharing to enable open innovation in industrial data ecosystems. Based on a rigorous multi-method study at ecosystem, firm and managerial levels, a framework of generative open innovation to govern multilateral data sharing will be developed. By addressing data-sharing barriers at all levels, the framework helps create collective value at the ecosystem level and capture a portion of that value at the firm and managerial levels. This should enable participants in industrial data ecosystems to share data confidently and unlock the full potential of open innovation for Australia’s digital economy, with estimated benefits of $315bn over the next decade. Read moreRead less
Privacy-Aware and Personalised Explanation Overlays for Recommender Systems. AI-powered recommender systems provide recommendations for daily lives, but they need to be legally interpretable and explainable. This project aims to transform existing black-box recommender models into transparent and trustworthy decision-support systems. The resulting tools will offer granular, explorable rationales for the recommendations in real time, creating greater public confidence while advancing the field. ....Privacy-Aware and Personalised Explanation Overlays for Recommender Systems. AI-powered recommender systems provide recommendations for daily lives, but they need to be legally interpretable and explainable. This project aims to transform existing black-box recommender models into transparent and trustworthy decision-support systems. The resulting tools will offer granular, explorable rationales for the recommendations in real time, creating greater public confidence while advancing the field. The expected outcomes include graph embedding methods for capturing real-world relationships in all their messiness and complexity. The anticipated contributions include impartial and accountable recommender models that are resistant to adversarial attacks and that slow the spread of misinformation.Read moreRead less