Evaluating and developing the evidence-base and data mining approaches to strengthen the consumer product safety system in Australia. Consumer product-related injuries cause over 173,000 injuries per year though there is limited evidence about the causes and risks to enable early identification and warnings for consumers. This project will evaluate the evidence-base and develop new methods to support an early identification and surveillance system for product-related injuries.
Learned Academies Special Projects - Grant ID: LA170100011
Funder
Australian Research Council
Funding Amount
$170,000.00
Summary
The use of big data for social policy: benefits and risks. This project aims to investigate the benefits and risks of using ‘big data’ and analytics for social policy. Drawing on documentary sources and interviews with experts and stakeholders, the project will use five case studies to examine the capability, underlying assumptions and possible impacts of such techniques. The project will bring together a multi-disciplinary team of social and data scientists to identify the infrastructural, tech ....The use of big data for social policy: benefits and risks. This project aims to investigate the benefits and risks of using ‘big data’ and analytics for social policy. Drawing on documentary sources and interviews with experts and stakeholders, the project will use five case studies to examine the capability, underlying assumptions and possible impacts of such techniques. The project will bring together a multi-disciplinary team of social and data scientists to identify the infrastructural, technical, and social, ethical and legal issues that need to be addressed. The project will define key issues for future research, promote collaboration between the social sciences and big data disciplines, while creating opportunities for building capability for researchers in the social sciences. Read moreRead less
Sentient buildings. This project aims to unite outputs from the large and varied array of sensors deployed in buildings into a coherent whole. By coordinating detections of resources and personnel from multiple sensors, it intends to enable more efficient allocation of shared resources within a public site such as a hospital, and enable a more effective emergency response. It intends to also allow the building to adapt over time to the way it is used, or to changing conditions. This is expected ....Sentient buildings. This project aims to unite outputs from the large and varied array of sensors deployed in buildings into a coherent whole. By coordinating detections of resources and personnel from multiple sensors, it intends to enable more efficient allocation of shared resources within a public site such as a hospital, and enable a more effective emergency response. It intends to also allow the building to adapt over time to the way it is used, or to changing conditions. This is expected to benefit the Australian construction industry as well as building operators, giving them a valuable export commodity. It intends also to benefit inhabitants of the buildings by providing a more safe, secure and accommodating environment.Read moreRead less
New methods for modelling and forecasting risk. The project will develop and assess risk measures and risk forecasting. It will assess why customary measures failed in the financial crisis and develop new and better techniques. The project is unique in terms of the scope and range of methods to be applied and tested. It will be of value to investors, institutions and regulators alike.
Developing a truly intelligent water meter through advanced data analytics. Developing a truly intelligent water meter through advanced data analytics. This project aims to develop intelligent pattern recognition algorithms using international data sets to autonomously categorise household water consumption data into end-uses (e.g. showers, leaks). Despite intelligent meters, big data chokes rather than enables decision making for customers and utilities. This project will resolve information sy ....Developing a truly intelligent water meter through advanced data analytics. Developing a truly intelligent water meter through advanced data analytics. This project aims to develop intelligent pattern recognition algorithms using international data sets to autonomously categorise household water consumption data into end-uses (e.g. showers, leaks). Despite intelligent meters, big data chokes rather than enables decision making for customers and utilities. This project will resolve information synthesis concerns using a combination of non-linear blind source separation techniques adapted from the pattern recognition, signal processing and decision science fields. Expected outcomes are that utilities will be leaders of sustainable water use in the information age, and that customers can use phones to access real-time data of water consumption.Read moreRead less
Cost efficient scheduling of big data application workflows on cloud through information correlation. Information correlation in and between big data application workflows scheduled on the cloud can help to significantly reduce overall scheduling costs by avoiding the execution of many correlated workflow activities. This project aims to systematically investigate such correlation for cost efficient scheduling. The expected outcomes are: establishing information correlation based scheduling rese ....Cost efficient scheduling of big data application workflows on cloud through information correlation. Information correlation in and between big data application workflows scheduled on the cloud can help to significantly reduce overall scheduling costs by avoiding the execution of many correlated workflow activities. This project aims to systematically investigate such correlation for cost efficient scheduling. The expected outcomes are: establishing information correlation based scheduling research and practical solutions for this important cloud and big data research area; benefiting key big data application areas on the cloud, such as hospitals, insurance companies and government information services; and helping to maintain Australia at the forefront of cloud and big data research with innovative industry applications.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE140100679
Funder
Australian Research Council
Funding Amount
$395,220.00
Summary
Real-time query processing over multi-dimensional uncertain data streams. Real-time query processing of multi-dimensional uncertain data streams is fundamental in many applications such as environmental monitoring and location based services. This project aims to develop effective techniques to explore the massive multi-dimensional uncertain data streams in real time. The project will develop, analyse, implement and evaluate novel indexing and query processing techniques to effectively and effic ....Real-time query processing over multi-dimensional uncertain data streams. Real-time query processing of multi-dimensional uncertain data streams is fundamental in many applications such as environmental monitoring and location based services. This project aims to develop effective techniques to explore the massive multi-dimensional uncertain data streams in real time. The project will develop, analyse, implement and evaluate novel indexing and query processing techniques to effectively and efficiently support a set of primitive queries including rank-based queries, dominance-based queries and proximity-based queries. The results of this project will be an important complement to the development of data stream systems and will bring considerable social, economic and technological benefits to Australia.Read moreRead less
Monitoring social events for user online behaviour analytics. This project aims to investigate the influence of public attention on steering user online behaviour. The exponential growth of online behaviour data makes online behaviour analytics increasingly important in social, commercial and political environments, but existing methods rely on user profiles only. The project will unify external social events with user profiles for behaviour analytics, and develop approaches for event database i ....Monitoring social events for user online behaviour analytics. This project aims to investigate the influence of public attention on steering user online behaviour. The exponential growth of online behaviour data makes online behaviour analytics increasingly important in social, commercial and political environments, but existing methods rely on user profiles only. The project will unify external social events with user profiles for behaviour analytics, and develop approaches for event database indexing, event-influenced behaviour modelling and prediction. The success of this project is expected to enhance users’ online experience and improve e-commerce’s market value.Read moreRead less
Taming the uncertainty in trajectory data. This project aims to develop effective and efficient methods to manage large scale uncertain trajectory data. It provides individuals, business, government and social groups the ability to explore significant uncertain trajectories and their patterns, for important usages in location based services, logistic, transportation and tourism.
Deep attribute-aware hashing for cross retrieval. This project aims to enable individuals, industries and governments, to freely access vital and linked information carried in different media types from different sources by developing a deep attribute-aware framework that embeds heterogeneous features into a shared data space to achieve effective and efficient cross system information retrieval. With the proliferation of heterogeneous data sources, there is an urgent need to enable search and re ....Deep attribute-aware hashing for cross retrieval. This project aims to enable individuals, industries and governments, to freely access vital and linked information carried in different media types from different sources by developing a deep attribute-aware framework that embeds heterogeneous features into a shared data space to achieve effective and efficient cross system information retrieval. With the proliferation of heterogeneous data sources, there is an urgent need to enable search and retrieval across different media types and domains. The framework developed by the project uses deep learning methods to develop meaningful image attributes to positively bridge the modality gap and the domain gap when hash functions are affixed to data. This project will significantly advance the research of multimedia retrieval, and benefit a series of related research problems whenever heterogeneous multimedia data are involved in their applications.Read moreRead less