Privacy Preserving Data Sharing in Electronic Health Environment. This project aims to improve access to electronic health data (EHD) while still ensuring patient privacy. EHD can provide important information for medical research and health-care resource allocations. However, data sharing in electronic health environments is challenging because of the privacy concerns of customers. Large-scale unauthorised access from internal staff has been reported in Medicare. This project aims to develop ne ....Privacy Preserving Data Sharing in Electronic Health Environment. This project aims to improve access to electronic health data (EHD) while still ensuring patient privacy. EHD can provide important information for medical research and health-care resource allocations. However, data sharing in electronic health environments is challenging because of the privacy concerns of customers. Large-scale unauthorised access from internal staff has been reported in Medicare. This project aims to develop new privacy-preserving algorithms on EHD database federations, which can provide efficient data access yet block inside attacks. It will significantly improve the data available for medical research, while reducing the cost of EHD system management and providing visualised decision supports to medical staff and the government health resource planners.Read moreRead less
Algorithms for collaborative micro-navigation based on spatio-temporal data management and data mining. Traffic congestion coupled with greenhouse gas emissions is a major challenge for modern society. This project will tackle this challenge by developing computer-assisted smart vehicles that can access and exchange real-time information about traffic conditions, leading to improved driving experience, safety and environmental sustainability.
Discovery Early Career Researcher Award - Grant ID: DE140100387
Funder
Australian Research Council
Funding Amount
$349,179.00
Summary
Mining Patterns and Changes of Wave Shapes for Efficiently Querying Periodic Data Streams. Many data streams change periodically, such as vital physiological parameters (for example, heart rate, arterial pressure and respiratory impedance) and seasonal environmental data streams (for example, temperature and turbidity of river water). However, the querying of periodic data streams faces great challenges, including the issue of critical signals being generally buried within massive data while cri ....Mining Patterns and Changes of Wave Shapes for Efficiently Querying Periodic Data Streams. Many data streams change periodically, such as vital physiological parameters (for example, heart rate, arterial pressure and respiratory impedance) and seasonal environmental data streams (for example, temperature and turbidity of river water). However, the querying of periodic data streams faces great challenges, including the issue of critical signals being generally buried within massive data while critical changes between similar wave shapes are difficult to recognise due to shifting, scaling and noise. This project will develop new mining algorithms to resolve these challenges by segmenting periodic wave shapes, discovering shape patterns and shape changes, and summarising raw data streams so that the summarised data can directly answer various user queries for efficiency.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE130100911
Funder
Australian Research Council
Funding Amount
$339,434.00
Summary
Accurate and online abnormality detection in multiple correlated time series. This study will develop a new kernel-based and online support vector regression method for real-time and correlated multiple time series and promote their use in critical applications, which will save money and lives. Examples include the detection of stock market crisis events and detection of patients' condition deterioration in the operating theatre.
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 painless approach to support efficient querying and mining of spatial data through smart transformations. This project will develop spatial data retrieval methods that are not only highly efficient but also easy to implement ('painless'). It will help businesses such as digital map providers, location based service providers and medical researchers quickly possess this key enabling technique for their large scale spatial querying and mining needs.
Continuous intent tracking for virtual assistance using big contextual data. Recently launched Virtual Assistant products such as Amazon Echo and Google Home are commanded by voice and can call apps to do simple tasks like setting timers and playing music. The next-generation virtual assistants will recommend things to be done proactively rather than waiting for commands passively. This project aims to develop algorithms that can predict what a user intends to do and therefore help virtual assis ....Continuous intent tracking for virtual assistance using big contextual data. Recently launched Virtual Assistant products such as Amazon Echo and Google Home are commanded by voice and can call apps to do simple tasks like setting timers and playing music. The next-generation virtual assistants will recommend things to be done proactively rather than waiting for commands passively. This project aims to develop algorithms that can predict what a user intends to do and therefore help virtual assistants make recommendations that suit users’ needs accurately. It will benefit many service industry sectors of Australia by enabling virtual assistants to provide services proactively.Read moreRead less
Biclique discovery in Big Data. This project aims to design algorithms to capture Big Data. Biclique is a popular graph model that can capture important cohesive structures in many applications. However, traditional biclique discovery algorithms which only focus on simple, small-scale, static and deterministic data are inadequate in the era of Big Data where data has Variety (various formats), Volume (large quantity), Velocity (dynamic update) and Veracity (uncertainty). This project expects to ....Biclique discovery in Big Data. This project aims to design algorithms to capture Big Data. Biclique is a popular graph model that can capture important cohesive structures in many applications. However, traditional biclique discovery algorithms which only focus on simple, small-scale, static and deterministic data are inadequate in the era of Big Data where data has Variety (various formats), Volume (large quantity), Velocity (dynamic update) and Veracity (uncertainty). This project expects to benefit real applications in both public and private sectors and add value to Australian manufactured products.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