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
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
Deep Pattern Mining for Brain Graph Analysis: A Data Mining Perspective. This project brings together experts in the fields of data mining and cognitive neuroscience. This project aims to develop new data analytics tools, algorithms, and models to combine complex multi-source neuroimage brain data and non-imaging data, to explore the interplays among these different data structures and identify novel functional patterns from complex brain graph structures. The research undertaken in this project ....Deep Pattern Mining for Brain Graph Analysis: A Data Mining Perspective. This project brings together experts in the fields of data mining and cognitive neuroscience. This project aims to develop new data analytics tools, algorithms, and models to combine complex multi-source neuroimage brain data and non-imaging data, to explore the interplays among these different data structures and identify novel functional patterns from complex brain graph structures. The research undertaken in this project expects to provide practical data analysis approaches and establish the theoretical foundations for data mining with multiple sources of brain data.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210101458
Funder
Australian Research Council
Funding Amount
$387,141.00
Summary
Scalable and Deep Anomaly Detection from Big Data with Similarity Hashing. Anomaly detection, aiming to identify anomalous but insightful patterns in data mining, is an important big data analytics technique. The nature of big data requires a detection method that can handle fast-evolving data of diverse types. However, existing methods suffer from either high computational cost or low detection performance. This project aims to develop a detection framework to advance detection performance and ....Scalable and Deep Anomaly Detection from Big Data with Similarity Hashing. Anomaly detection, aiming to identify anomalous but insightful patterns in data mining, is an important big data analytics technique. The nature of big data requires a detection method that can handle fast-evolving data of diverse types. However, existing methods suffer from either high computational cost or low detection performance. This project aims to develop a detection framework to advance detection performance and efficiency, based on a novel deep learning model called deep isolation forest which is different from the traditional artificial neural network based models. The outcome will bring huge benefits to various applications such as real-time predictive maintenance in smart manufacturing, and intrusion detection in cybersecurity.Read moreRead less
Big data fast response: real-time classification of big data stream. This project will provide a big data stream based data mining framework to build a real-time monitoring and decision making platform for business and government big data. By clever use of smart information, this breakthrough science will provide frontier technologies for managing and using huge data for social and economic benefits globally.
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.
Enabling User-Centric Wisdom Engines for Big Information Network Search. Big information networks, for example social networks, are important for modern information systems, yet searching for useful information from huge networks is difficult because network structure and user relationships continuously evolve. This project will provide theoretical foundations for structural knowledge mining to enable user-centric wisdom search on big information networks. Expected outcomes are: real-world appli ....Enabling User-Centric Wisdom Engines for Big Information Network Search. Big information networks, for example social networks, are important for modern information systems, yet searching for useful information from huge networks is difficult because network structure and user relationships continuously evolve. This project will provide theoretical foundations for structural knowledge mining to enable user-centric wisdom search on big information networks. Expected outcomes are: real-world application platform to support information network analysis; theories for big network control, algorithms, and systematic solutions to enable user-centric knowledge search, including a new search engine for big information networks. By significantly improving IT, it will benefit Australian business, industry and the wider community.Read moreRead less
Mining multi-typed and dynamic graphs. Large volumes of data collected nowadays from real-world applications are often represented as graphs. The nodes and the edges of such graphs represent different types of entities and interactions, and they have time information. This project will develop algorithms that mine efficiently such multi-typed and dynamic graphs.
From Data to Artefact: a Key Ingredient in Service Interoperation. Supporting service interoperation in the e-Business environment is crucial in automating business transactions across organisation boundaries. If no proper mechanism is in place, business delays, failures, and serious disputes can occur. This project will explore new avenues to this long-standing and challenging problem by providing an artefact framework to model and manage business collaboration. Given this project's unique pers ....From Data to Artefact: a Key Ingredient in Service Interoperation. Supporting service interoperation in the e-Business environment is crucial in automating business transactions across organisation boundaries. If no proper mechanism is in place, business delays, failures, and serious disputes can occur. This project will explore new avenues to this long-standing and challenging problem by providing an artefact framework to model and manage business collaboration. Given this project's unique perspective and approaches that are directly applicable to existing enterprise systems, there is a strong potential for its results to lead to a new generation of e-Business design and management, advance the knowledge base of the discipline and yield high returns to the Australian service society and IT industry.Read moreRead less
Escaping the concurrency trade-off: a new approach to enterprise software. Enterprise software manages the operations of all business and government organisations. Designers of this often rely on their intuition or luck, by using high-performance database facilities whose correctness is not guaranteed. This project will show designers how to use these facilities while still having the assurance that the data will not be corrupted. This will improve the quality of the data used by Australian ent ....Escaping the concurrency trade-off: a new approach to enterprise software. Enterprise software manages the operations of all business and government organisations. Designers of this often rely on their intuition or luck, by using high-performance database facilities whose correctness is not guaranteed. This project will show designers how to use these facilities while still having the assurance that the data will not be corrupted. This will improve the quality of the data used by Australian enterprises, and thus improve their operations. Australian software designers will also benefit, as they will be able to produce software that combines high performance with assurance that concurrency errors will not occur.Read moreRead less