Special Research Initiatives - Grant ID: SR0567393
Funder
Australian Research Council
Funding Amount
$100,000.00
Summary
Infrastructure for large-scale data resource sharing between research institutions – an environmental case study. The project creates a federated distributed data infrastructure for research, that encourages data creators to make their data available to other scientists, and encourages users to make use of data available from many sources. The vision is to establish an ICT infrastructure to facilitate a whole-of-environment approach to environmental research. The outcome is a proof-of-concept ....Infrastructure for large-scale data resource sharing between research institutions – an environmental case study. The project creates a federated distributed data infrastructure for research, that encourages data creators to make their data available to other scientists, and encourages users to make use of data available from many sources. The vision is to establish an ICT infrastructure to facilitate a whole-of-environment approach to environmental research. The outcome is a proof-of-concept application based upon a case study of Queensland Environmental Protection Agency’s databases, to gain an in-depth understanding of the complexity, scope and key technological barriers for establishing an ICT infrastructure, to identify where the latest technologies can be used and where the gaps are for these technologies to be used in environmental sciences.Read moreRead less
Next-generation spatial keyword search. Spatial keyword search is like a normal web search plus additional location information, which helps users to obtain a better ranking of results by considering the spatial proximity. The success of this project will deliver a next-generation spatial keyword search system that overcomes the severe usability limitations experienced by users today.
Smart comparison and assessment of prediction models for better health using next generation data mining. Prediction models can be used to provide early warning of events, such as adverse medical outcomes. This project will develop principles for the smart management of large collections of prediction models using data mining, enabling more timely medical interventions for Australians to live healthier and longer.
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
Implementing Bioinformatics Algorithms using .NET-based Stored Procedures in a Database Cluster. We will create the technology for significantly improving the management, processing and sharing of biological data. Areas in which Australia has a large stake, including the development of new drugs, disease research, and agricultural genetic engineering, stand to benefit considerably from these advances. This contribution by Australian researchers to a global problem will have a positive impact on ....Implementing Bioinformatics Algorithms using .NET-based Stored Procedures in a Database Cluster. We will create the technology for significantly improving the management, processing and sharing of biological data. Areas in which Australia has a large stake, including the development of new drugs, disease research, and agricultural genetic engineering, stand to benefit considerably from these advances. This contribution by Australian researchers to a global problem will have a positive impact on our own health industry, and will provide the foundation for improvements in agriculture and financial services.Read moreRead less
Efficient databases for flash memory. This project will bring significant benefits to Australia. It will place Australia at the fore-front of the emerging field of flash memory databases. The lack of prior research in this field, coupled with the significant performance benefits of flash memory, means this project will make significant breakthroughs in this frontier technology. The algorithms developed will be directly incorporated into a state-of-the-art open source database and disseminated ....Efficient databases for flash memory. This project will bring significant benefits to Australia. It will place Australia at the fore-front of the emerging field of flash memory databases. The lack of prior research in this field, coupled with the significant performance benefits of flash memory, means this project will make significant breakthroughs in this frontier technology. The algorithms developed will be directly incorporated into a state-of-the-art open source database and disseminated throughout the international community, thereby boosting Australia's international research reputation. The database developed will significantly increase the speed of large database servers as well as bring significant energy savings to databases running on small portable devices.Read moreRead less
Improving the Effectiveness of Conceptual Model Validation Work. Errors or omissions in conceptual models often lead to significant problems when information systems are being built. Prior research has shown the cost of fixing the consequences of such errors or omissions grows exponentially as a function of how late they are discovered. Thus, significant economic benefits arise if they are identified early in the system development process. The project outcomes will facilitate early prevention ....Improving the Effectiveness of Conceptual Model Validation Work. Errors or omissions in conceptual models often lead to significant problems when information systems are being built. Prior research has shown the cost of fixing the consequences of such errors or omissions grows exponentially as a function of how late they are discovered. Thus, significant economic benefits arise if they are identified early in the system development process. The project outcomes will facilitate early prevention and detection of errors of omissions in conceptual models. They will also contribute to attainment of the national priority goal of smart information use through improved data management.Read moreRead less
Managing private location data in a mobile and networked world: getting the balance right. Location based data are transforming the mobile service industry and this project will develop novel approaches to safeguard the location privacy of mobile individuals. This will facilitate the development of privacy-aware services which can be used for real time traffic monitoring, care for the elderly and smartphone enabled location services.
Effective and efficient record linkage with transformation rules. Record linkage is an enabling technology for organisations to identify and remove 'redundant' entries in their databases; this helps prevent data quality problems that may cost millions. This project will deliver the next-generation record linkage methodology that enables cost and time economical linkage beyond what is currently possible.
Locality sensitive hashing for big data. This project aims to solve problems to applying locality sensitive hashing (LSH) to Big Data, namely handling new similarity functions, large data volume and better efficiency. LSH is one of the most widely adopted methods for answering similarity queries, and used widely in computer science. The project is expected to provide frontier technology to applications to combat crimes in the cybersecurity space, and lead to more intelligent and real-time analys ....Locality sensitive hashing for big data. This project aims to solve problems to applying locality sensitive hashing (LSH) to Big Data, namely handling new similarity functions, large data volume and better efficiency. LSH is one of the most widely adopted methods for answering similarity queries, and used widely in computer science. The project is expected to provide frontier technology to applications to combat crimes in the cybersecurity space, and lead to more intelligent and real-time analysis of Big Data.Read moreRead less