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
Developing an effective defence to cyber-reputation manipulation attacks. This project will develop new technologies for businesses to accurately identify fake internet reviews. Fake reviews, paid for and/or written with malicious intent, can cause irreparable damage to businesses resulting in revenue loss, consumer dissatisfaction or even closure of businesses. However they are difficult to identify, as they continuously evolve to avoid detection and the volume of Internet reviews makes analysi ....Developing an effective defence to cyber-reputation manipulation attacks. This project will develop new technologies for businesses to accurately identify fake internet reviews. Fake reviews, paid for and/or written with malicious intent, can cause irreparable damage to businesses resulting in revenue loss, consumer dissatisfaction or even closure of businesses. However they are difficult to identify, as they continuously evolve to avoid detection and the volume of Internet reviews makes analysis a monumental task. This project will provide advanced tools to detect fake website reviews and a cybersecurity system prototype ready to be used by industry, making Australia a leader in this field and resulting in a safer internet environment for all.Read moreRead less
Industrial Transformation Training Centres - Grant ID: IC200100022
Funder
Australian Research Council
Funding Amount
$4,883,406.00
Summary
ARC Training Centre for Information Resilience. The proposed centre aims at building workforce capacity in Australian organisations to create, protect and sustain agile data pipelines, capable of detecting and responding to failures and risks across the information value chain in which the data is sourced, shared, transformed, analysed and consumed. Building on strong foundations of responsible data science, the centre will bring together end-users, technology providers, and cutting-edge researc ....ARC Training Centre for Information Resilience. The proposed centre aims at building workforce capacity in Australian organisations to create, protect and sustain agile data pipelines, capable of detecting and responding to failures and risks across the information value chain in which the data is sourced, shared, transformed, analysed and consumed. Building on strong foundations of responsible data science, the centre will bring together end-users, technology providers, and cutting-edge research, to lift the socio-technical barriers to data driven transformation and develop resilient data pipelines capable of delivering game-changing productivity gains that position Australian organisations at the forefront of technology leadership and value creation from data assets. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE190101118
Funder
Australian Research Council
Funding Amount
$339,000.00
Summary
High performance density-based clustering in parallel environments. This project aims to conduct a comprehensive study on density-based clustering to improve data management in parallel computing environments. Clustering, a fundamental task in data management, is to group a set of objects such that objects in the same group (called a cluster) are more similar to each other than those in other groups in order to simplify retrieval of similar information. Clustering is widely used in many fields i ....High performance density-based clustering in parallel environments. This project aims to conduct a comprehensive study on density-based clustering to improve data management in parallel computing environments. Clustering, a fundamental task in data management, is to group a set of objects such that objects in the same group (called a cluster) are more similar to each other than those in other groups in order to simplify retrieval of similar information. Clustering is widely used in many fields including machine learning, pattern recognition, information retrieval, bioinformatics and image analysis. It is expected that the developed clustering techniques will provide significant performance improvements in industry sectors where decisions are made based on clustering data analytics, such as the sectors of finance, renewable energy and artificial intelligence.Read moreRead less
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
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
Data-driven Traffic Analytics for Incident Analysis and Management. Traffic incidents are among the primary concerns of all transport authorities around the world due to their significant impact in terms of traffic congestion and delay, air and noise pollution, and management cost. This project aims to address incident analysis and management in complex and multi-modal traffic networks by combining multidisciplinary research efforts from transportation engineering and data science. The intended ....Data-driven Traffic Analytics for Incident Analysis and Management. Traffic incidents are among the primary concerns of all transport authorities around the world due to their significant impact in terms of traffic congestion and delay, air and noise pollution, and management cost. This project aims to address incident analysis and management in complex and multi-modal traffic networks by combining multidisciplinary research efforts from transportation engineering and data science. The intended outcomes will be an innovative incident analysis and management framework synergising traffic data analytics and traffic simulation modelling as well as its key enabling techniques and prototype systems. This will significantly help mitigate incident impacts on daily commuters.Read moreRead less
A ubiquitous system for indoor location-based services. This project aims to design a system capable of providing location-based services in WiFi-enabled indoor buildings. The project expects to address two major challenges hindering such a system, capability to identify a user's location in any indoor venue with minimum manual overhead, and effective data management techniques for indoor data. Expected outcomes of this project include new techniques for managing and utilising indoor location da ....A ubiquitous system for indoor location-based services. This project aims to design a system capable of providing location-based services in WiFi-enabled indoor buildings. The project expects to address two major challenges hindering such a system, capability to identify a user's location in any indoor venue with minimum manual overhead, and effective data management techniques for indoor data. Expected outcomes of this project include new techniques for managing and utilising indoor location data and enhanced international collaborations. The project will support and enhance a wide range of indoor applications such as emergency services, assisted healthcare systems, indoor asset tracking, event planning, indoor venue management, and consumer experience.Read moreRead less