Efficient spatial data management for enabling true ride-sharing. This data management project aims to examine ride-sharing as a model of a complex decision system that can be optimised to deliver better outcomes. Popular ride-sharing apps have quickly evolved into ride-sourcing services that are comparable to calling a taxi on a mobile phone. Such arrangements miss many of the key benefits of true ride-sharing for the society. The project will model incentives by helping people agree on points ....Efficient spatial data management for enabling true ride-sharing. This data management project aims to examine ride-sharing as a model of a complex decision system that can be optimised to deliver better outcomes. Popular ride-sharing apps have quickly evolved into ride-sourcing services that are comparable to calling a taxi on a mobile phone. Such arrangements miss many of the key benefits of true ride-sharing for the society. The project will model incentives by helping people agree on points of interest rather than directly seeking trips from others to set destinations. It also aims to introduce privacy-aware dynamic matching of sharers, and expand to transportation at large, to generate new shared transportation services. The expected outcome of this project is to elevate today's taxi-like ride-sharing services to true ride-sharing arrangements. This is expected to provide benefits such as reduced traffic and emissions, as well as addressing parking issues and other traffic problems.Read moreRead less
Personalised data analytics for the Internet of Me. This project aims to develop data mining methods for extracting comprehensive personalised knowledge, without breaching trust. The Internet of Things will lead to the Internet of Me. Billions of smart devices connected to the Internet record people’s lives. Companies wish to provide highly personalised services that engage their customers, while individuals wish to understand their health, lifestyle, education and personal performance. The chal ....Personalised data analytics for the Internet of Me. This project aims to develop data mining methods for extracting comprehensive personalised knowledge, without breaching trust. The Internet of Things will lead to the Internet of Me. Billions of smart devices connected to the Internet record people’s lives. Companies wish to provide highly personalised services that engage their customers, while individuals wish to understand their health, lifestyle, education and personal performance. The challenge is to analyse individuals’ personal data, and discover how they differentiate from and overlap with others’. This project expects to enable businesses to deepen customer satisfaction and individuals to better understand their personal place in a connected world.Read moreRead less
Fast effective clustering technologies for highly dynamic massive networks. Clustering is a fundamental data mining and analysis task. In an interconnected evolving world, friendships and information flows are modelled as large dynamic networks. Structural clustering and correlation clustering are important and well-studied approaches for static networks; for evolving networks, where links appear and disappear over time, we lack efficient techniques. Anticipated outcomes are new practical cluste ....Fast effective clustering technologies for highly dynamic massive networks. Clustering is a fundamental data mining and analysis task. In an interconnected evolving world, friendships and information flows are modelled as large dynamic networks. Structural clustering and correlation clustering are important and well-studied approaches for static networks; for evolving networks, where links appear and disappear over time, we lack efficient techniques. Anticipated outcomes are new practical clustering algorithms for dynamic networks – with performance guarantees of efficiency and clustering quality – and prototype software, guiding us to pick a good clustering. Expected benefits include better understanding of spread in evolving social networks, accelerating the software testing cycle, and improved topic detection.Read moreRead less
Advancing Analytical Query Processing with Urban Trajectory Data. This project aims to provide accurate, rapid, and comprehensive information to analyze transport and related infrastructure use in real time. This project expects to develop innovative solutions by exploiting massive urban trajectory data derived from public transport usage, route mapping, GPS tracking and road-side sensors. Expected outcomes include a new algorithmic framework to support complex trajectory-driven analytical tasks ....Advancing Analytical Query Processing with Urban Trajectory Data. This project aims to provide accurate, rapid, and comprehensive information to analyze transport and related infrastructure use in real time. This project expects to develop innovative solutions by exploiting massive urban trajectory data derived from public transport usage, route mapping, GPS tracking and road-side sensors. Expected outcomes include a new algorithmic framework to support complex trajectory-driven analytical tasks in public transport network planning, traffic congestion prevention, and facility deployment. This should significantly benefit both government and industry in data-driven decision makings and evaluations on the impact of decisions made, and ultimately materialize Australian government’s Smart Cities Plan.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
Empowering Next-Generation Spatial Digital Twins with Linked Spatial Data. This project aims to design novel algorithms for aligning and querying of spatial data from heterogeneous sources. Spatial data is being generated at an unprecedented rate due to the prevalence of mobile devices and ubiquitous connectivity, which enables a novel application, spatial digital twins. However, harnessing this data in spatial digital twins is hampered by the isolation of data from different sources. The projec ....Empowering Next-Generation Spatial Digital Twins with Linked Spatial Data. This project aims to design novel algorithms for aligning and querying of spatial data from heterogeneous sources. Spatial data is being generated at an unprecedented rate due to the prevalence of mobile devices and ubiquitous connectivity, which enables a novel application, spatial digital twins. However, harnessing this data in spatial digital twins is hampered by the isolation of data from different sources. The project will investigate algorithms to align and query spatial data from heterogeneous sources for high accessibility. It will enable novel applications with advanced spatial analytical querying needs, such as emergency planning, benefiting location-based service providers, urban planners, and emergency management agencies.Read moreRead less
Next Generation Spatial Data Management for Virtual Spatial Systems. This project aims to design novel spatial data retrieval methods for efficient and accurate querying of large datasets with location information. Spatial data is being generated at an unprecedented rate due to the prevalence of mobile devices and ubiquitous connectivity. However, harnessing this data is hampered by outdated and inefficient methods. The project will investigate data retrieval methods that self-optimise for high ....Next Generation Spatial Data Management for Virtual Spatial Systems. This project aims to design novel spatial data retrieval methods for efficient and accurate querying of large datasets with location information. Spatial data is being generated at an unprecedented rate due to the prevalence of mobile devices and ubiquitous connectivity. However, harnessing this data is hampered by outdated and inefficient methods. The project will investigate data retrieval methods that self-optimise for high query efficiency and accuracy, by utilising underlying real-world data patterns. It will enable novel applications for virtual spatial systems with large-scale querying needs, such as spatial digital twins and metaverses, benefiting location-based service providers, urban planners, and emergency management agencies.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
Next-generation Intelligent Explorations of Geo-located Data . This project aims to build a next-generation intelligent exploration framework over massive geo-located data, varying from points-of-interest to areas-of-interest data, in order to dramatically enhance user experiences when interacting with various forms of geo-located data over maps. Expected outcomes include novel exploration models, efficient and scalable algorithms for retrieving and visualizing the exploration results, online up ....Next-generation Intelligent Explorations of Geo-located Data . This project aims to build a next-generation intelligent exploration framework over massive geo-located data, varying from points-of-interest to areas-of-interest data, in order to dramatically enhance user experiences when interacting with various forms of geo-located data over maps. Expected outcomes include novel exploration models, efficient and scalable algorithms for retrieving and visualizing the exploration results, online updating of personal preferences during the life cycle of exploration, as well as a prototype system to evaluate and demonstrate practical value of the research. It will complement existing map services and significantly benefit many location-aware services, e.g., logistics, health services and urban planning.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