The Southern Sky Survey. The Southern Sky Survey will create the first digital map of the southern sky, providing a community available database of a billion objects. This database will be used by entire community, with our group targetting four key areas including: studying the creation of our solar system through a census of distant asteroids, exploring how stars and planets form by observing nearby young stars, probing the shape and extent of the Galaxy's dark matter halo, and discovering whe ....The Southern Sky Survey. The Southern Sky Survey will create the first digital map of the southern sky, providing a community available database of a billion objects. This database will be used by entire community, with our group targetting four key areas including: studying the creation of our solar system through a census of distant asteroids, exploring how stars and planets form by observing nearby young stars, probing the shape and extent of the Galaxy's dark matter halo, and discovering when the first stars in the Universe formed. This data set will also be used as a staging ground for a Virtual Observatory.
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View-based processing of pattern matching queries in large graphs. Graph data exist ubiquitously in modern information systems. Graph pattern matching (GPM) finds parts of the data graph that match a given pattern. It has applications in many areas including knowledge discovery, public health, and crime detection. This project will develop novel techniques for the efficient processing of GPM queries in large graphs.
Robust Preference Inference from Spatial-Temporal Interaction Networks. This project aims to develop innovative techniques for effectively and efficiently managing user preference profiles from less labelled, sparse and noisy interaction data. A unified novel learning framework along with a set of data analysis techniques are expected to be developed from this project, which will provide a non-intrusive way of conducting predictive analysis on user preference profiling via discovering human expl ....Robust Preference Inference from Spatial-Temporal Interaction Networks. This project aims to develop innovative techniques for effectively and efficiently managing user preference profiles from less labelled, sparse and noisy interaction data. A unified novel learning framework along with a set of data analysis techniques are expected to be developed from this project, which will provide a non-intrusive way of conducting predictive analysis on user preference profiling via discovering human explicit and implicit interest domains. The expected results of this application will not only maintain Australia's leadership in this frontier research area, but also support many important applications that safeguard Australian people and economy such as cyber security, healthcare, and e-Commerce.Read moreRead less
On effectively modelling and efficiently discovering communities from large networks. Finding and maintaining close communities from very large scale, dynamically changing networks is interesting and challenging. This project aims to develop new techniques to identify such communities as fast as possible through exploiting the rich semantics and individual relationships within the communities.
Pattern Discovery of Discriminating Behaviour Associated with Hidden Communities. A sound understanding of discriminating behaviour in hidden communities, e.g. market manipulation, is essential for effective intervention and prevention. This project will deliver novel and workable algorithms and tools for modelling and pattern discovery of such behaviour. This will safeguard Australia by tackling crucial business and social issues like abnormal trading, online crime and terrorism, thereby enhanc ....Pattern Discovery of Discriminating Behaviour Associated with Hidden Communities. A sound understanding of discriminating behaviour in hidden communities, e.g. market manipulation, is essential for effective intervention and prevention. This project will deliver novel and workable algorithms and tools for modelling and pattern discovery of such behaviour. This will safeguard Australia by tackling crucial business and social issues like abnormal trading, online crime and terrorism, thereby enhancing public confidence, compliance and security in both the economy and society, by preventing and reducing economic and social impact. It will create skills and outcomes to further Australia's leadership in managing emerging data mining challenges and applications, and will deepen collaboration with eminent researchers worldwide.Read moreRead less
Creating the social genome: Advanced techniques for linking dynamic data. This project aims to develop novel efficient and effective models and techniques that enable record linkage of large dynamic databases while preserving the privacy of sensitive personal data. Social genomes are the digital footprints of our society. They are the basis of population informatics, which is revolutionising how researchers in various domains conduct studies, governments plan services and expenditures, and busin ....Creating the social genome: Advanced techniques for linking dynamic data. This project aims to develop novel efficient and effective models and techniques that enable record linkage of large dynamic databases while preserving the privacy of sensitive personal data. Social genomes are the digital footprints of our society. They are the basis of population informatics, which is revolutionising how researchers in various domains conduct studies, governments plan services and expenditures, and businesses advertise and interact with their customers. A core requirement of population informatics is the linking of large dynamic databases that contain details about people from diverse sources. The expected outcomes of this project will provide novel solutions to the challenges of population informatics faced by Australian organisations.Read moreRead less
Privacy-preserving record linkage on multiple large databases. Record linkage has been recognised as a crucial infrastructure component in many information systems, however privacy concerns commonly prevent the linking of databases that contain personal information. This project will develop techniques that will enable the linking of multiple large databases without revealing any private information.