Scaling Disk-Resident Learned Indexes For Database Systems. This project aims to investigate new disk-resident learned indexing algorithms to store and process data in database systems by advancing the state-of-the-art in memory-resident learned modeling. This project expects to generate new knowledge in the area of digital storage technologies utilising novel and efficient techniques in learned indexing for big data. This should provide significant benefits to enable modern database systems to ....Scaling Disk-Resident Learned Indexes For Database Systems. This project aims to investigate new disk-resident learned indexing algorithms to store and process data in database systems by advancing the state-of-the-art in memory-resident learned modeling. This project expects to generate new knowledge in the area of digital storage technologies utilising novel and efficient techniques in learned indexing for big data. This should provide significant benefits to enable modern database systems to scale with the massive growth of data, improve the efficiency of data processing, improve the effectiveness of projects that utilise big data, and dramatically reduce energy costs in Australian data centres when storing and retrieving data from databases and lower their carbon footprints.Read moreRead less
Beyond Query: Exploratory Subgraph Discovery and Search System. Exploring co-working user groups in dynamic network data is a vital challenge in many applications, for example, in online education. This project aims to discover new relationships of users and compute their co-working performance in continuous time periods. The outcomes of the project are to design effective subgraph exploratory models, three novel types of subgraph search solutions, and devise a friendly exploratory subgraph sear ....Beyond Query: Exploratory Subgraph Discovery and Search System. Exploring co-working user groups in dynamic network data is a vital challenge in many applications, for example, in online education. This project aims to discover new relationships of users and compute their co-working performance in continuous time periods. The outcomes of the project are to design effective subgraph exploratory models, three novel types of subgraph search solutions, and devise a friendly exploratory subgraph search system for supporting the real-time network data analytics. The success of the project will make a significant contribution to the scientific foundation of graph data mining and its applications in data engineering domains, as well as benefiting co-working performance of people in Australian labor markets.Read moreRead less
Big temporal graph processing in the Cloud. This project aims to develop efficient and scalable algorithms to process big temporal graphs in the Cloud. In particular, we will investigate three most representative types of queries over big temporal graphs including vertex-based queries, path-based queries, and subgraph-based queries. Expected outcomes of this project include theoretical foundations and scalable algorithms to process big temporal graphs as well as a system prototype for evaluation ....Big temporal graph processing in the Cloud. This project aims to develop efficient and scalable algorithms to process big temporal graphs in the Cloud. In particular, we will investigate three most representative types of queries over big temporal graphs including vertex-based queries, path-based queries, and subgraph-based queries. Expected outcomes of this project include theoretical foundations and scalable algorithms to process big temporal graphs as well as a system prototype for evaluation and to demonstrate the practical value. Success in this project should see significant benefits for many important applications such as cybersecurity, e-commerce, health and road networks.Read moreRead less
Next-Generation Distributed Graph Engine for Big Graphs. This project aims to develop an efficient and scalable distributed graph engine to process big graphs. In particular, we will investigate the foundations for the distributed real-time graph engine, focusing on graph storage and graph operators, and then provide solutions for a set of representative graph mining and query processing tasks. Expected outcomes of this project include theoretical foundations and a scalable real-time graph engin ....Next-Generation Distributed Graph Engine for Big Graphs. This project aims to develop an efficient and scalable distributed graph engine to process big graphs. In particular, we will investigate the foundations for the distributed real-time graph engine, focusing on graph storage and graph operators, and then provide solutions for a set of representative graph mining and query processing tasks. Expected outcomes of this project include theoretical foundations and a scalable real-time graph engine to process big graphs as well as a system prototype for evaluation and to demonstrate the practical value. Success in this project should see significant benefits for many important applications such as cybersecurity, e-commerce, health and road networks.Read moreRead less
Driving Towards Greener and Safer Roads using Big Spatiotemporal Data. This project aims to design novel techniques for using big spatiotemporal data to reduce the impact of road transport on the environment and improve road safety. This project expects to address key challenges and lay scientific foundations of using the big data for developing a next-generation eco-friendly navigation system and increasing situational awareness for road transport safety. Expected outcomes of this project inclu ....Driving Towards Greener and Safer Roads using Big Spatiotemporal Data. This project aims to design novel techniques for using big spatiotemporal data to reduce the impact of road transport on the environment and improve road safety. This project expects to address key challenges and lay scientific foundations of using the big data for developing a next-generation eco-friendly navigation system and increasing situational awareness for road transport safety. Expected outcomes of this project include novel big data management and analytics techniques, and new edge computing models for vehicular networks. The success of this project should bring several key benefits including reducing greenhouse gas emissions on roads, facilitating urban planning, and improving road safety.Read moreRead less
Next-Generation OFDM Communication Systems: Analysis and Design for the Physical Layer. Next-generation orthogonal frequency-division multiplexed (OFDM) systems represent the future of broadband wireless access technology. Such systems are vital to Australia's future infrastructure and growing economy by providing more bandwidth with greater flexibility for new broadband applications. The research outcomes from this project will help enable future OFDM systems, and thus directly benefit Austra ....Next-Generation OFDM Communication Systems: Analysis and Design for the Physical Layer. Next-generation orthogonal frequency-division multiplexed (OFDM) systems represent the future of broadband wireless access technology. Such systems are vital to Australia's future infrastructure and growing economy by providing more bandwidth with greater flexibility for new broadband applications. The research outcomes from this project will help enable future OFDM systems, and thus directly benefit Australia. Development of cutting-edge information technology know-how will enhance Australia's international ICT reputation. Valuable research training of highly-skilled Australian students is another important benefit.Read moreRead less
Parallel and Distributed Machine Learning - Smart Data Analysis in the Multicore Era. In large data centres our research will lead to reduced energy consumption by using graphics cards which have a much better computation to power ratio than traditional processors. On desktop computers, it will make machine learning practical by enabling efficient algorithms for spam filtering and content analysis. On networked systems it will lead to distributed inference, caching and collaborative filtering ap ....Parallel and Distributed Machine Learning - Smart Data Analysis in the Multicore Era. In large data centres our research will lead to reduced energy consumption by using graphics cards which have a much better computation to power ratio than traditional processors. On desktop computers, it will make machine learning practical by enabling efficient algorithms for spam filtering and content analysis. On networked systems it will lead to distributed inference, caching and collaborative filtering applications which will both reduced the bandwidth required and make the internet safer for users. Finally, it will enable rapid deployment of sensor networks for monitoring and detection, such as for environmental monitoring and safeguarding Australia's borders.Read moreRead less
Optimal Transforms of Random Vectors. This proposal focusses on development of optimal transforms to describe and model nonlinear phenomena when only statistical information is known. An optimal transform is a mathematical procedure that enables us to process information in a way that is most suited to the task in hand. These transforms have been successfully used in approximation, information theory, communications, control theory and signal and image processing. Applications include modelli ....Optimal Transforms of Random Vectors. This proposal focusses on development of optimal transforms to describe and model nonlinear phenomena when only statistical information is known. An optimal transform is a mathematical procedure that enables us to process information in a way that is most suited to the task in hand. These transforms have been successfully used in approximation, information theory, communications, control theory and signal and image processing. Applications include modelling of physical, chemical and biological systems, filtering and compression of signals and data classification and clustering. We propose two new hybrid models for realistic transforms in a general structural framework.
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Optimum Multi-rate Filter Bank Design for Speech Enhancement and Communication Systems. Multi-rate signal processing is gaining more and more importance in signal processing applications such as echo cancellation, microphone arrays, speech enhancement and equalisation. This project is aimed at obtaining high performance and high efficiency multi-rate processing designs by developing appropriate problem formulations based on solid mathematical foundations so that powerful optimisation techniques ....Optimum Multi-rate Filter Bank Design for Speech Enhancement and Communication Systems. Multi-rate signal processing is gaining more and more importance in signal processing applications such as echo cancellation, microphone arrays, speech enhancement and equalisation. This project is aimed at obtaining high performance and high efficiency multi-rate processing designs by developing appropriate problem formulations based on solid mathematical foundations so that powerful optimisation techniques can be applied. The developed multi-rate systems will result in low complexity and low power hardware implementations without significant compromise in performance in each application. Size and cost sensitive of communication devices such as personal data assistants, mobile telephones, hands-free devices, and laptops will benefit greatly from the outcome of the project.Read moreRead less
Fast, practical and effective algorithms for clustering with advice. To maintain a safe and healthy society, government and industry need high quality immunization and national security databases. Since we cannot afford to have duplicate, incomplete and conflicting records that refer to the same person, we unify them by identifying clusters of related records.
In the emerging field of functional genomics, diagnosis of certain diseases is enhanced by determining which genes act together. Diffe ....Fast, practical and effective algorithms for clustering with advice. To maintain a safe and healthy society, government and industry need high quality immunization and national security databases. Since we cannot afford to have duplicate, incomplete and conflicting records that refer to the same person, we unify them by identifying clusters of related records.
In the emerging field of functional genomics, diagnosis of certain diseases is enhanced by determining which genes act together. Different experimental runs might result in different clusterings of genes: we need one consensus clustering that summarizes the experimental outcomes.
Cleaning databases and combining clusterings by hand would require vast amounts of time. This project will result in faster and more accurate computational procedures.Read moreRead less