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Please take a few minutes to provide your input. The survey closes COB Friday 29 May 2026.
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Trust-Oriented Data Analytics in Online Social Networks. Trust-oriented data analytics is essential in online social networks for reducing deceitful interactions and enhancing trust between users. This project aims to systematically devise innovative solutions by considering rich social contextual information as an important source of trust. The expected outcomes of this project include innovative solutions from a fundamental perspective to the challenges of context-aware trust propagation, trus ....Trust-Oriented Data Analytics in Online Social Networks. Trust-oriented data analytics is essential in online social networks for reducing deceitful interactions and enhancing trust between users. This project aims to systematically devise innovative solutions by considering rich social contextual information as an important source of trust. The expected outcomes of this project include innovative solutions from a fundamental perspective to the challenges of context-aware trust propagation, trust network searching/matching, and trustworthy/malicious user prediction in online social networks. This project is significant as it will advance the knowledge base for enabling a trustworthy social networking environment, benefiting billions of Australian and worldwide online social network users.Read moreRead less
Scaling Disk-Resident Learned Indexes For Database Systems. This project aims to investigate new disk-resident learned indexing algorithms to store and process data in database systems by advancing the state-of-the-art in memory-resident learned modeling. This project expects to generate new knowledge in the area of digital storage technologies utilising novel and efficient techniques in learned indexing for big data. This should provide significant benefits to enable modern database systems to ....Scaling Disk-Resident Learned Indexes For Database Systems. This project aims to investigate new disk-resident learned indexing algorithms to store and process data in database systems by advancing the state-of-the-art in memory-resident learned modeling. This project expects to generate new knowledge in the area of digital storage technologies utilising novel and efficient techniques in learned indexing for big data. This should provide significant benefits to enable modern database systems to scale with the massive growth of data, improve the efficiency of data processing, improve the effectiveness of projects that utilise big data, and dramatically reduce energy costs in Australian data centres when storing and retrieving data from databases and lower their carbon footprints.Read moreRead less
Differential Evolution Framework for Intelligent Charging Scheduling. Smart charging scheduling is a vital challenge as dynamic environment with traffic networks and various unexpected issues. This project aims to develop a differential evolution framework for intelligent charging scheduling. The framework consists of a comprehensive charging scheduling model with various road networks and factors. The project outcomes include a distributed evolutionary computation framework, differential evolut ....Differential Evolution Framework for Intelligent Charging Scheduling. Smart charging scheduling is a vital challenge as dynamic environment with traffic networks and various unexpected issues. This project aims to develop a differential evolution framework for intelligent charging scheduling. The framework consists of a comprehensive charging scheduling model with various road networks and factors. The project outcomes include a distributed evolutionary computation framework, differential evolution algorithms, and cooperative co-evolutionary strategies. The outcome results will be demonstrated by practical evaluations over public datasets and comparisons to related works. The project is beneficial to the nation in both theory of artificial intelligence techniques and applications of real transport systems.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
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
Searching Cohesive Subgraphs in Big Attributed Graph Data. The availability of big attributed graph data brings great opportunities for realizing big values of data. Making sense of such big attributed graph data finds many applications, including health, science, engineering, business, environment, etc. A cohesive subgraph, one of key components that captures the latent properties in a graph, is essential to graph analysis. This project aims to invent effective models of cohesive subgraphs and ....Searching Cohesive Subgraphs in Big Attributed Graph Data. The availability of big attributed graph data brings great opportunities for realizing big values of data. Making sense of such big attributed graph data finds many applications, including health, science, engineering, business, environment, etc. A cohesive subgraph, one of key components that captures the latent properties in a graph, is essential to graph analysis. This project aims to invent effective models of cohesive subgraphs and efficient algorithms for searching and monitoring cohesive subgraphs in big and dynamic attributed graphs from both structure and attribute perspectives. The methods, techniques, and prototype systems developed in this project can be deployed to facilitate the smart use of big graph data across the nation. Read moreRead less
Modelling and Searching Cohesive Groups over Heterogeneous Graphs . Heterogeneous information networks (HINs) contain richer structural and semantic information represented as different types of objects and links. Searching cohesive groups from HINs finds many applications and also brings challenges at both conceptual and technical levels. This project aims to investigate the effective modelling of cohesive groups that take both homogeneous and heterogeneous information into account for differen ....Modelling and Searching Cohesive Groups over Heterogeneous Graphs . Heterogeneous information networks (HINs) contain richer structural and semantic information represented as different types of objects and links. Searching cohesive groups from HINs finds many applications and also brings challenges at both conceptual and technical levels. This project aims to investigate the effective modelling of cohesive groups that take both homogeneous and heterogeneous information into account for different applications and devise efficient algorithms for searching and monitoring those cohesive groups based on different models. The methods, techniques, and evaluation systems developed in this project can be deployed to facilitate the smart use of heterogeneous information networks across the nation.Read moreRead less
Responsible modelling respecting privacy, data quality, and green computing. With the unprecedented growing impact of data on science, the economy and society, there comes the need for responsible data science practices which are accountable for the social good. This project aims to investigate the challenging problem of how to provide responsible data management, spanning across privacy-aware data exploration, resilient modelling to cope with imperfect data, and efficient model architectures fo ....Responsible modelling respecting privacy, data quality, and green computing. With the unprecedented growing impact of data on science, the economy and society, there comes the need for responsible data science practices which are accountable for the social good. This project aims to investigate the challenging problem of how to provide responsible data management, spanning across privacy-aware data exploration, resilient modelling to cope with imperfect data, and efficient model architectures for resource-constrained environments. This will be achieved by developing theories and techniques for complex real-world multi-modal data retrieval throughout the data life-cycle. The expected outcomes will significantly contribute to building capability in emerging technologies in the context of responsible data science. Read moreRead less
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
Privacy-Preserving Location Based Queries. This project aims to develop efficient solutions for mobile users to consume location-based services (LBS) without revealing their locations. The project expects to demonstrate the effectiveness of the solutions using theoretic analysis and practical experiments. The expected outcomes are a multiparty trust model, techniques to distribute user location information among multiple location-based services, and a practical system to protect privacy in mobil ....Privacy-Preserving Location Based Queries. This project aims to develop efficient solutions for mobile users to consume location-based services (LBS) without revealing their locations. The project expects to demonstrate the effectiveness of the solutions using theoretic analysis and practical experiments. The expected outcomes are a multiparty trust model, techniques to distribute user location information among multiple location-based services, and a practical system to protect privacy in mobile environments. This should protect the privacy of individuals and increase users’ trust in location-based systems.Read moreRead less