Privacy-preserving cloud data mining-as-a-service. This project aims to explore practical privacy-preserving solutions for cloud data mining-as-a-service based on the Intel Software Guard Extensions (SGX) technology. The research addresses privacy concerns of users when outsourcing data mining needs to the cloud. These concerns have increased as more businesses evaluate data mining-as-an outsourced service due to lack of expertise or computation resources. The expected outcomes from the research ....Privacy-preserving cloud data mining-as-a-service. This project aims to explore practical privacy-preserving solutions for cloud data mining-as-a-service based on the Intel Software Guard Extensions (SGX) technology. The research addresses privacy concerns of users when outsourcing data mining needs to the cloud. These concerns have increased as more businesses evaluate data mining-as-an outsourced service due to lack of expertise or computation resources. The expected outcomes from the research will include new data privacy models, new privacy-preserving data mining algorithms, and a prototype of cloud data mining software. These will help businesses cut costs for data mining and privacy protection, and provide significant benefits toward helping Australia achieve its national cyber security strategy and potentially provide economic impact from commercialisation of new software technology for the industry partner.Read moreRead less
Low-cost Sensing Methods and Hybrid Learning Models. This project aims to revolutionise the theory and practice of sensing and monitoring by developing novel Artificial Intelligence and Internet of Things technologies. This project expects to generate new knowledge in the area of Artificial Intelligence of Things by combining sensing, machine learning, and big data analytics. Expected outcomes of this project include novel low-cost sensing methods and new hybrid machine learning models for predi ....Low-cost Sensing Methods and Hybrid Learning Models. This project aims to revolutionise the theory and practice of sensing and monitoring by developing novel Artificial Intelligence and Internet of Things technologies. This project expects to generate new knowledge in the area of Artificial Intelligence of Things by combining sensing, machine learning, and big data analytics. Expected outcomes of this project include novel low-cost sensing methods and new hybrid machine learning models for predictive sensory data analytics. This should provide significant benefits, such as substantially reduced operating and service costs and improved accuracy for real-time monitoring in the fields where cheap-to-implement and easy-to-service monitoring systems over large geographical areas are imperative.Read moreRead less
Deep analytics of non-occurring but important behaviours. This project aims to build a systematic theory for the deep analytics of complex and important occurring and non-occurring behaviours. Behaviours that should occur but do not take place, called non-occurring behaviours (NOB), are widely evident but easily overlooked, such as missed important medical treatments. While often occurring behaviours are focused, such NOB may be associated with significant effects such as a threat to health. Thi ....Deep analytics of non-occurring but important behaviours. This project aims to build a systematic theory for the deep analytics of complex and important occurring and non-occurring behaviours. Behaviours that should occur but do not take place, called non-occurring behaviours (NOB), are widely evident but easily overlooked, such as missed important medical treatments. While often occurring behaviours are focused, such NOB may be associated with significant effects such as a threat to health. This project expects to fill the knowledge gaps in representing, analysing and evaluating NOB complexities and impact, with significant benefits for the evidence-based detection, prediction and risk management of covert NOB applications and their important effects.Read moreRead less
Deep Interaction Learning in Unlabelled Big Data and Complex Systems. This project aims to effectively model intricate interactions deeply embedded in unlabelled big data and complex systems, which are often hierarchical, heterogeneous, contextual, dynamic or even contrastive. Learning such interactions is the keystone of robust data science and for realizing the value of big data but it poses significant challenges and knowledge gaps to existing data analytics and learning systems. The expected ....Deep Interaction Learning in Unlabelled Big Data and Complex Systems. This project aims to effectively model intricate interactions deeply embedded in unlabelled big data and complex systems, which are often hierarchical, heterogeneous, contextual, dynamic or even contrastive. Learning such interactions is the keystone of robust data science and for realizing the value of big data but it poses significant challenges and knowledge gaps to existing data analytics and learning systems. The expected outcomes include new-generation theories and methods for the unsupervised learning of complex interactions in real-life big data, which are anticipated to enable the intrinsic processing of big data complexities and substantially enhance Australia’s leadership in frontier data science research and applications. Read moreRead less
Robust Federated Learning for Imperfect Decentralised Data. This project aims to develop a next-generation robust federated learning framework to tackle the challenging scenarios of imperfect decentralised data in real applications, e.g. mobile phones and the Internet of Things (IoT) devices. The outcomes will bring great benefits to a broad range of industry sectors by providing novel large-scale intelligent applications with privacy preservation. The proposed method will advance the developmen ....Robust Federated Learning for Imperfect Decentralised Data. This project aims to develop a next-generation robust federated learning framework to tackle the challenging scenarios of imperfect decentralised data in real applications, e.g. mobile phones and the Internet of Things (IoT) devices. The outcomes will bring great benefits to a broad range of industry sectors by providing novel large-scale intelligent applications with privacy preservation. The proposed method will advance the development of a cutting-edge technique to develop new intelligent applications in a decentralised and privacy-sensitive scenario. This game-changing research will advance current data mining and artificial intelligence research from centralised intelligence to decentralised intelligence with a collaboration network.Read moreRead less
Coupling Learning in Big Data. Big data features complex coupling relationships within and between diverse entities in various forms and layers. This fundamentally challenges existing learning theories, which usually assume that data is independent and identically distributed (IID). This indicates that such IID tools may either be inapplicable for big data or capture an incomplete or even biased picture of the ground truth in big data. Hence, this project aims to invent breakthrough theories and ....Coupling Learning in Big Data. Big data features complex coupling relationships within and between diverse entities in various forms and layers. This fundamentally challenges existing learning theories, which usually assume that data is independent and identically distributed (IID). This indicates that such IID tools may either be inapplicable for big data or capture an incomplete or even biased picture of the ground truth in big data. Hence, this project aims to invent breakthrough theories and effective tools for systematically modelling and learning sophisticated couplings embedded in big data applications. The outcomes are expected to enhance Australia's leading role in data science research and lift data intelligence-driven productivity and economic growth in a changing world.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
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.
Next-generation techniques for analysing massive data sets. To process enormous amounts of data, leading computing companies are turning to modern computing frameworks, for which little theory of efficient computational techniques has been developed. This project will resolve key theoretical questions and provide fast techniques for poorly understood pattern recognition and bioinformatics problems.
Effective and Efficient Data Quality Management for Data Lakes. This project aims to enhance the quality and completeness for data in data lakes by innovative and judicious use of Database and Artificial Intelligence techniques. To achieve the aim, we will develop knowledge-enhanced error correction during data ingestion, flexible and efficient data exploration, and heterogeneity-tolerant scalable data integration solutions. Its significance lies in integrating techniques from both database and ....Effective and Efficient Data Quality Management for Data Lakes. This project aims to enhance the quality and completeness for data in data lakes by innovative and judicious use of Database and Artificial Intelligence techniques. To achieve the aim, we will develop knowledge-enhanced error correction during data ingestion, flexible and efficient data exploration, and heterogeneity-tolerant scalable data integration solutions. Its significance lies in integrating techniques from both database and artificial intelligence areas to deliver effective solutions for challenging problems in data lakes. The outcome of this project will provide new knowledge in this cutting-edge domain, and provide additional value and immediate benefits to all applications built upon data lakes. Read moreRead less