XML Views of Relational Databases: Semantics and Update Problems. XML is the standard for representing, publishing and exchanging data over the Internet and relational database is the dominant technology for data management. Updating XML views over relational data is fundamental to bring these two technologies together to serve Internet-based applications. Australia has been a leading country in both developing and applying internet technologies. The theoretic outcomes of this project will contr ....XML Views of Relational Databases: Semantics and Update Problems. XML is the standard for representing, publishing and exchanging data over the Internet and relational database is the dominant technology for data management. Updating XML views over relational data is fundamental to bring these two technologies together to serve Internet-based applications. Australia has been a leading country in both developing and applying internet technologies. The theoretic outcomes of this project will contribute to the advance in database and web research communities and establish us as an internationally leading group in this research area. The technological outcomes will help organisations in Australia effectively and efficiently conduct e-Business on the Internet. Read moreRead less
Accurate Context-Aware Search for Managed Document Collections. Search is a key component of a vast range of computing applications. However, while search on large collections of general-purpose text is well-understood, and the best systems are highly effective, search on smaller or special-purpose collections is much less reliable and has attracted relatively little research. By identifying general ways of using context such as user history or prior page usefulness, the quality of search in s ....Accurate Context-Aware Search for Managed Document Collections. Search is a key component of a vast range of computing applications. However, while search on large collections of general-purpose text is well-understood, and the best systems are highly effective, search on smaller or special-purpose collections is much less reliable and has attracted relatively little research. By identifying general ways of using context such as user history or prior page usefulness, the quality of search in such cases can be greatly improved. Products that make use of these principles will provide greater workplace efficiency and be able to locate information that other search tools cannot identify.Read moreRead less
ARC Centre for Nanostructured Electromaterials. Electromaterials transport electrons or ions and facilitate charge transfer, underpinning most energy capture/storage processes and cell communication. We propose a national Centre to develop nanostructured electromaterials with exceptional properties. The Centre aims to synthesise novel nanomaterials and assemble them into innovative nanoscale devices. We will exploit these materials to enhance performance in energy conversion/storage systems (eg. ....ARC Centre for Nanostructured Electromaterials. Electromaterials transport electrons or ions and facilitate charge transfer, underpinning most energy capture/storage processes and cell communication. We propose a national Centre to develop nanostructured electromaterials with exceptional properties. The Centre aims to synthesise novel nanomaterials and assemble them into innovative nanoscale devices. We will exploit these materials to enhance performance in energy conversion/storage systems (eg. photovoltaics, batteries, including wearable systems), and novel energy transfer in bioapplications (eg. Bionic Ear). These advances, together with the resource of trained personnel, will assist Australian industry to exploit this exciting area.Read moreRead less
Using Past Queries for Fast and Accurate Web Searching. Searching the entire Internet, or a company web site, has become a vital task for modern organisations. While there has been significant research into improving search engines through using web pages themselves, very little attention has been paid to improving web search by exploiting the vast numbers of queries that users submit to search engines each day. This project will use state of the art compression and algorithmic techniques to imp ....Using Past Queries for Fast and Accurate Web Searching. Searching the entire Internet, or a company web site, has become a vital task for modern organisations. While there has been significant research into improving search engines through using web pages themselves, very little attention has been paid to improving web search by exploiting the vast numbers of queries that users submit to search engines each day. This project will use state of the art compression and algorithmic techniques to improve the speed and accuracy of web search using data gleaned from millions of Internet queries (provided under agreement by Microsoft). Improving search engines will have a direct benefit to many Australian industries, and support the government's priority area of "smart information use".Read moreRead less
Effective Information Retrieval for Partitioned Document Collections. Current information retrieval services make use of massive indexes in order to resolve content-based queries. Monolithic approaches like this have been effective until now because the volume of data stored has been manageable on a single machine or tightly-coupled cluster of machines, and because the data has been available for collection. But with an increasing amount of automatically generated data, and an increasing diversi ....Effective Information Retrieval for Partitioned Document Collections. Current information retrieval services make use of massive indexes in order to resolve content-based queries. Monolithic approaches like this have been effective until now because the volume of data stored has been manageable on a single machine or tightly-coupled cluster of machines, and because the data has been available for collection. But with an increasing amount of automatically generated data, and an increasing diversity of information sources, other approaches are required. In this project we will investigate mechanisms for handling retrieval tasks when the indexes to the data are stored locally with the data, and when no central index is viable.Read moreRead less
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
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
Differential Evolution Framework for Intelligent Charging Scheduling. Smart charging scheduling is a vital challenge as dynamic environment with traffic networks and various unexpected issues. This project aims to develop a differential evolution framework for intelligent charging scheduling. The framework consists of a comprehensive charging scheduling model with various road networks and factors. The project outcomes include a distributed evolutionary computation framework, differential evolut ....Differential Evolution Framework for Intelligent Charging Scheduling. Smart charging scheduling is a vital challenge as dynamic environment with traffic networks and various unexpected issues. This project aims to develop a differential evolution framework for intelligent charging scheduling. The framework consists of a comprehensive charging scheduling model with various road networks and factors. The project outcomes include a distributed evolutionary computation framework, differential evolution algorithms, and cooperative co-evolutionary strategies. The outcome results will be demonstrated by practical evaluations over public datasets and comparisons to related works. The project is beneficial to the nation in both theory of artificial intelligence techniques and applications of real transport systems.Read moreRead less
Searching Cohesive Subgraphs in Big Attributed Graph Data. The availability of big attributed graph data brings great opportunities for realizing big values of data. Making sense of such big attributed graph data finds many applications, including health, science, engineering, business, environment, etc. A cohesive subgraph, one of key components that captures the latent properties in a graph, is essential to graph analysis. This project aims to invent effective models of cohesive subgraphs and ....Searching Cohesive Subgraphs in Big Attributed Graph Data. The availability of big attributed graph data brings great opportunities for realizing big values of data. Making sense of such big attributed graph data finds many applications, including health, science, engineering, business, environment, etc. A cohesive subgraph, one of key components that captures the latent properties in a graph, is essential to graph analysis. This project aims to invent effective models of cohesive subgraphs and efficient algorithms for searching and monitoring cohesive subgraphs in big and dynamic attributed graphs from both structure and attribute perspectives. The methods, techniques, and prototype systems developed in this project can be deployed to facilitate the smart use of big graph data across the nation. Read moreRead less
Modelling and Searching Cohesive Groups over Heterogeneous Graphs . Heterogeneous information networks (HINs) contain richer structural and semantic information represented as different types of objects and links. Searching cohesive groups from HINs finds many applications and also brings challenges at both conceptual and technical levels. This project aims to investigate the effective modelling of cohesive groups that take both homogeneous and heterogeneous information into account for differen ....Modelling and Searching Cohesive Groups over Heterogeneous Graphs . Heterogeneous information networks (HINs) contain richer structural and semantic information represented as different types of objects and links. Searching cohesive groups from HINs finds many applications and also brings challenges at both conceptual and technical levels. This project aims to investigate the effective modelling of cohesive groups that take both homogeneous and heterogeneous information into account for different applications and devise efficient algorithms for searching and monitoring those cohesive groups based on different models. The methods, techniques, and evaluation systems developed in this project can be deployed to facilitate the smart use of heterogeneous information networks across the nation.Read moreRead less