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
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
Contextual Behabiour Predictions in Dynamic Mobile E-commerce. The project aims to address behaviour prediction and develop novel techniques and tools for modelling, predicting human behaviours and making effective recommendations based on ubiquitous user behaviour data in mobile e-commerce. The techniques enable multi-source data fusion, context learning and model adaptation, and dynamic recommendation with interpretability ability. Expected outcomes include advances in data analytics theory an ....Contextual Behabiour Predictions in Dynamic Mobile E-commerce. The project aims to address behaviour prediction and develop novel techniques and tools for modelling, predicting human behaviours and making effective recommendations based on ubiquitous user behaviour data in mobile e-commerce. The techniques enable multi-source data fusion, context learning and model adaptation, and dynamic recommendation with interpretability ability. Expected outcomes include advances in data analytics theory and informed decision-making. This provides significant benefits of not only placing Australia in the forefront of exploiting multimodal user behaviour big data in dynamic e-commerce but also transforming Australian government and businesses to intelligent and contextual services adaptive to complex situations.Read moreRead less
Adaptive Key-value Store for Future Extreme Heterogeneous Systems. Safe, lasting storage of data, and efficient access to it, is vital for all aspects of computing, ranging from e-commerce applications, and data-management in governments. For the storage of data, persistent key-value stores are central in modern computing platforms. However, contemporary key-value stores have not been designed for emerging extreme heterogeneous computational systems with future hardware accelerators and storage ....Adaptive Key-value Store for Future Extreme Heterogeneous Systems. Safe, lasting storage of data, and efficient access to it, is vital for all aspects of computing, ranging from e-commerce applications, and data-management in governments. For the storage of data, persistent key-value stores are central in modern computing platforms. However, contemporary key-value stores have not been designed for emerging extreme heterogeneous computational systems with future hardware accelerators and storage capabilities, including graphics processor and flash-based memory. This project will devise an adaptive key-value store framework for heterogeneous systems. Our new framework will adaptively harvest the performance potential of future hardware such that applications can cope with fast-growing data sets.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
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