Federated Cloud Services Configuration and Orchestration. Cloud computing allows organisations to expand or contract their computing footprint based on existing demand. However, existing cloud delivery models support individual segregated and heterogeneous functionalities, which prevent effective coordinated combination of on-premise and off-premise applications, services, and resources. This project aims to significantly contribute to the scientific foundations for the model-driven and elastic ....Federated Cloud Services Configuration and Orchestration. Cloud computing allows organisations to expand or contract their computing footprint based on existing demand. However, existing cloud delivery models support individual segregated and heterogeneous functionalities, which prevent effective coordinated combination of on-premise and off-premise applications, services, and resources. This project aims to significantly contribute to the scientific foundations for the model-driven and elastic configuration and orchestration of resources over heterogeneous cloud services. The outcomes of the project aim to contribute to lifting productivity and economic growth through interoperable and elastic cloud service technologies as well as delivering appropriate skills for the new digital economy.Read moreRead less
Learned Academies Special Projects - Grant ID: LA170100025
Funder
Australian Research Council
Funding Amount
$210,000.00
Summary
Big data in Australian research: issues, challenges and opportunities. This project aims to enhance discovery, productivity and translation within and between disciplines in Australian research through better utilisation of big data. Advances in our ability to capture, store, process and analyse large data sets are transforming many parts of society, including the research sector. Machine learning, for example, will allow data-driven analysis of massive, unstructured data sets such as social med ....Big data in Australian research: issues, challenges and opportunities. This project aims to enhance discovery, productivity and translation within and between disciplines in Australian research through better utilisation of big data. Advances in our ability to capture, store, process and analyse large data sets are transforming many parts of society, including the research sector. Machine learning, for example, will allow data-driven analysis of massive, unstructured data sets such as social media archives or financial records in ways that will shed new light on many complex research questions. This project will map existing capability and infrastructure within and across disciplines, identify common and discipline-specific practices, challenges and opportunities, and assess technology, infrastructure, training and collaboration priorities for Australia.
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Studying privacy protection methods for multiple independent data releases. Privacy is at risk if two or more published data sets contain overlapping individuals even when each data set is anonymised. This project will investigate if existing anonymisation methods can handle this privacy risk, and will study new solutions. The outcomes will potentially have a great impact on data anonymisation research and applications.
Algorithmic engineering and complexity analysis of protocols for consensus. Opinions, rankings, observations, votes, gene sequences, sensor-networks in security systems or climate models. Massive datasets and the ability to share information at unprecedented speeds, makes finding the most central representative, the Consensus Problem, extremely complex. This research delivers new insights and new, efficient algorithms.