Transaction Oriented Computational Models for Multi Agent Systems. Agent systems are a very promising technology for constructing complex, large-scale software. Australian researchers have made key
contributions in this area, particularly with reference to one mature and commonly adopted agent architecture known as BDI (Belief, Desire, Intention). To make this technology suitable for use in advanced applications, it has to be provided with robust and predictable behaviour. This project wil ....Transaction Oriented Computational Models for Multi Agent Systems. Agent systems are a very promising technology for constructing complex, large-scale software. Australian researchers have made key
contributions in this area, particularly with reference to one mature and commonly adopted agent architecture known as BDI (Belief, Desire, Intention). To make this technology suitable for use in advanced applications, it has to be provided with robust and predictable behaviour. This project will address that need by designing and implementing a novel agent language for BDI, based on contributions using transactional concepts for agents developed at The University of Melbourne. This will contribute to the development of robust and predictable agent software, that can be used in complex and large scale applications of the future.
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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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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.
Automated assessment of data quality in biological knowledge resources. This project aims to develop methods for identifying poor quality data in biological databases. Research in biomedicine is underpinned by massive databases of biological data. Data quality is largely managed through manual curation, but automated methods to assess quality are critically needed. This project expects to develop a suite of computational tools for assessing biological data quality, utilising an innovative approa ....Automated assessment of data quality in biological knowledge resources. This project aims to develop methods for identifying poor quality data in biological databases. Research in biomedicine is underpinned by massive databases of biological data. Data quality is largely managed through manual curation, but automated methods to assess quality are critically needed. This project expects to develop a suite of computational tools for assessing biological data quality, utilising an innovative approach based on network analysis of database record connectivity. These tools will enable quantifying data quality at scale. Researchers, evidence-based decision-makers in biomedicine, and the analytical or predictive tools that use this data will make more reliable inferences and decisions.Read moreRead less
Investigation and Development of Parallel Large Scale Record Linkage Techniques. Record linkage aims at matching records of the same entity (like customer or patient) in large (administrative) databases. The outcomes of the proposed research will improve current techniques in terms of efficiency, accuracy and the need for human intervention. Through experimental studies and stochastic modelling the performance of traditional and new methods for data cleaning, standardisation and linkage will be ....Investigation and Development of Parallel Large Scale Record Linkage Techniques. Record linkage aims at matching records of the same entity (like customer or patient) in large (administrative) databases. The outcomes of the proposed research will improve current techniques in terms of efficiency, accuracy and the need for human intervention. Through experimental studies and stochastic modelling the performance of traditional and new methods for data cleaning, standardisation and linkage will be assessed. The effect of the statistical dependency of attribute values will be studied. New methods using clustering for blocking large datasets, and predictive models including interaction terms will be implemented, analysed and evaluated on high-performance computers and office-based PC clusters.
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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
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
Democratising Big Machine Learning. Technological advances such as cloud computing have disrupted thousands of businesses managing volatile compute loads. While elements of Big Data are now everywhere, still absent are wide-spread solutions for learning from data at scale-Big Machine Learning, the ultimate goal of Big Data. The greatest problems come not from a lack of distributed machine learning algorithms, but rather from preparing the data needed for fitting, evaluating and applying statisti ....Democratising Big Machine Learning. Technological advances such as cloud computing have disrupted thousands of businesses managing volatile compute loads. While elements of Big Data are now everywhere, still absent are wide-spread solutions for learning from data at scale-Big Machine Learning, the ultimate goal of Big Data. The greatest problems come not from a lack of distributed machine learning algorithms, but rather from preparing the data needed for fitting, evaluating and applying statistical models; often a manual, messy and costly process. This project proposes to develop advanced databases and statistical techniques for scalable and efficient data preparation, with the goal of bringing Big Machine Learning to a much broader range of users and businesses.Read moreRead less
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