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
Managing data with high redundancy and low value density. This project aims to develop a database for data storage, cleaning, compression, hierarchal summarisation, indexing and query processing for machination data.Database management systems are needed to support stream query processing and manage historical data to support complex data analytics, data mining and data-driven decision making. Machination data, often found in sensor networks, GPS and RFID applications, vehicle on-board devices a ....Managing data with high redundancy and low value density. This project aims to develop a database for data storage, cleaning, compression, hierarchal summarisation, indexing and query processing for machination data.Database management systems are needed to support stream query processing and manage historical data to support complex data analytics, data mining and data-driven decision making. Machination data, often found in sensor networks, GPS and RFID applications, vehicle on-board devices and medical monitoring devices are difficult to manage and process because of large volumes and streaming, high redundancy and low value density. This project is expected to stream machination data management to support scalable query processing and data analytics.Read moreRead less
Decentralised Collaborative Predictive Analytics on Personal Smart Devices. This project tackles the challenging problem of personalised predictive analytics with resource-constrained personal devices and massive-scale data. The knowledge to be generated concerns privacy, fairness, and resource efficiency in the era of Internet of Things. The expected outcomes include a collaborative learning paradigm for building personalised models on personal smart devices in open and fully decentralised sett ....Decentralised Collaborative Predictive Analytics on Personal Smart Devices. This project tackles the challenging problem of personalised predictive analytics with resource-constrained personal devices and massive-scale data. The knowledge to be generated concerns privacy, fairness, and resource efficiency in the era of Internet of Things. The expected outcomes include a collaborative learning paradigm for building personalised models on personal smart devices in open and fully decentralised settings. Privacy and model fairness are core tenets of the paradigm. Personalised predictive analytics is frontier research that will position Australia at the forefront of AI and give business the tools needed to deploy innovative business systems for market exploitation with a secure, equitable and competitive advantage.Read moreRead less
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.
Making Spatiotemporal Data More Useful: An Entity Linking Approach. This project aims to establish a methodology for spatiotemporal entity linking by utilising object movement traces to support database integration and data quality management for the next-generation of data where spatiotemporal attributes are ubiquitous. It expects to develop a novel entity linking paradigm for automatic, efficient and reliable spatiotemporal data integration together with a new data privacy study in this contex ....Making Spatiotemporal Data More Useful: An Entity Linking Approach. This project aims to establish a methodology for spatiotemporal entity linking by utilising object movement traces to support database integration and data quality management for the next-generation of data where spatiotemporal attributes are ubiquitous. It expects to develop a novel entity linking paradigm for automatic, efficient and reliable spatiotemporal data integration together with a new data privacy study in this context. Expected outcome include new database technologies for data signature generation and similarity-based search, and improved location data privacy protection methods. This project should provide significant benefits to all areas where high quality spatiotemporal data fusion is essential to meaningful data analysis.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210100160
Funder
Australian Research Council
Funding Amount
$423,000.00
Summary
Information Extraction from Large-scale Low-quality Data. Information extraction which identifies entities and relations from data is a key technology that lays the foundation for understanding the semantics of data. This project aims to investigate the problem of information extraction by innovatively exploring the informality and temporal evolution of data. It expects to develop novel techniques for reliable, efficient, and scalable information discovery from large-scale low-quality data. Expe ....Information Extraction from Large-scale Low-quality Data. Information extraction which identifies entities and relations from data is a key technology that lays the foundation for understanding the semantics of data. This project aims to investigate the problem of information extraction by innovatively exploring the informality and temporal evolution of data. It expects to develop novel techniques for reliable, efficient, and scalable information discovery from large-scale low-quality data. Expected outcomes include a set of collective, contextualised, and temporal-aware algorithms for information extraction and integration, built on top of effective indexing and in-parallel processing. This project is anticipated to benefit a considerable number of data-driven intelligence-based applications.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101465
Funder
Australian Research Council
Funding Amount
$419,498.00
Summary
Minimising Human Efforts to Fight Fake News and Restore the Public Trust. Our modern society is struggling with an unprecedented amount of online fake news, which is recently driven by misused artificial intelligence (AI) technologies. This project aims to build the first real-time system integrating algorithmic models and human validators to counter such falsehoods, especially those AI-fabricated false stories. This project expects to deliver a series of cost-effective and streaming methods emp ....Minimising Human Efforts to Fight Fake News and Restore the Public Trust. Our modern society is struggling with an unprecedented amount of online fake news, which is recently driven by misused artificial intelligence (AI) technologies. This project aims to build the first real-time system integrating algorithmic models and human validators to counter such falsehoods, especially those AI-fabricated false stories. This project expects to deliver a series of cost-effective and streaming methods empowering a Web-based observatory dashboard of fake news propagation. This achieves significant benefits for media organisations, governments, the public, and academia via timely alerts, data-journalism reports, and novel data visualisations of social media landscape to distinguish between legitimate and deceptive contents.Read moreRead less
Challenging big data for scalable, robust and real-time recommendations. With the advent of big data era, recommender systems are facing unprecedented challenges with respect to the four dimensions of big data: big volume, low veracity, high velocity and high variety. This project aims to develop a new generation of cost-effective techniques for scalable, robust and real-time recommendations utilising big data. This project aims to address these challenges to achieve scalable, robust and real-ti ....Challenging big data for scalable, robust and real-time recommendations. With the advent of big data era, recommender systems are facing unprecedented challenges with respect to the four dimensions of big data: big volume, low veracity, high velocity and high variety. This project aims to develop a new generation of cost-effective techniques for scalable, robust and real-time recommendations utilising big data. This project aims to address these challenges to achieve scalable, robust and real-time recommendations. This project will devise a series of cost-effective machine learning methods and schemes to deliver an end-to-end recommender framework. This project has the potential to significantly reduce the energy consumption of large-scale recommender systems as well as facilitating an increase in the use of recommendation applications for big data.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101610
Funder
Australian Research Council
Funding Amount
$403,398.00
Summary
Towards Explainable Multi-source Multivariate Time-series Analysis. The aim of this project is to build deep learning models with transparent reasoning behind the results that can be easily interpreted by humans. The research rests on translating pertinent knowledge from multiple sources of complex data containing event sequences into graph form and embedding those knowledge graphs into a sophisticated deep learning model. Such an accomplishment represents the next great advance in machine intel ....Towards Explainable Multi-source Multivariate Time-series Analysis. The aim of this project is to build deep learning models with transparent reasoning behind the results that can be easily interpreted by humans. The research rests on translating pertinent knowledge from multiple sources of complex data containing event sequences into graph form and embedding those knowledge graphs into a sophisticated deep learning model. Such an accomplishment represents the next great advance in machine intelligence and will lay the theoretical foundations for building intelligent analysis tools that truly work in tandem with people. The potential benefits to science, society, and the Australian economy, particularly in finance, sensor technologies, and emergency health services would be appreciable.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE130101311
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Predicting health status of geriatric patients from user trusted multimedia observations. The information technology developed in this project will provide health care specialists with a better window into the lives of elderly patients. Their behaviour can then be accurately interpreted, potentially leading to earlier recognition of problems and better treatment.