Automating real-time feedback in virtual reality training through data mining. This project will use data mining techniques to develop a real-time feedback system that can be used in virtual reality training environments. This system will not only improve trainees' learning, it will also lead to more efficient use of virtual reality training in industries such as aviation, aerospace, mining, health and emergency services.
Industrial Transformation Training Centres - Grant ID: IC200100022
Funder
Australian Research Council
Funding Amount
$4,883,406.00
Summary
ARC Training Centre for Information Resilience. The proposed centre aims at building workforce capacity in Australian organisations to create, protect and sustain agile data pipelines, capable of detecting and responding to failures and risks across the information value chain in which the data is sourced, shared, transformed, analysed and consumed. Building on strong foundations of responsible data science, the centre will bring together end-users, technology providers, and cutting-edge researc ....ARC Training Centre for Information Resilience. The proposed centre aims at building workforce capacity in Australian organisations to create, protect and sustain agile data pipelines, capable of detecting and responding to failures and risks across the information value chain in which the data is sourced, shared, transformed, analysed and consumed. Building on strong foundations of responsible data science, the centre will bring together end-users, technology providers, and cutting-edge research, to lift the socio-technical barriers to data driven transformation and develop resilient data pipelines capable of delivering game-changing productivity gains that position Australian organisations at the forefront of technology leadership and value creation from data assets. Read moreRead less
Efficient processing of distance-based spatial queries on multi-valued objects. This project aims to develop effective and efficient algorithms to analyse large scale multi-valued objects. The success of this project will not only be an important complement to the current spatial database systems but also bring considerable economic and social benefits to Australia.
Scalable Composition and Deployment of Business-to-Business Web-based E-Services. The ubiquity of the Internet offers far reaching opportunities for
emerging Web applications. Separate businesses may be linked into a virtual enterprise that provides a one-stop shop for users. However, there is no model available for managing electronically accessible
services (e-services) in a systematic manner which make them easy to compose and deploy. Our research proposes to address the fundamental issue ....Scalable Composition and Deployment of Business-to-Business Web-based E-Services. The ubiquity of the Internet offers far reaching opportunities for
emerging Web applications. Separate businesses may be linked into a virtual enterprise that provides a one-stop shop for users. However, there is no model available for managing electronically accessible
services (e-services) in a systematic manner which make them easy to compose and deploy. Our research proposes to address the fundamental issues related to fast and scalable composition of e-services in Web-based environments. We propose novel concepts for integrating a large number of dynamic and loosely coupled services. Another distinctive feature of our approach is the peer-to-peer collaborative execution of services.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
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE180100158
Funder
Australian Research Council
Funding Amount
$348,026.00
Summary
A large-scale distributed experimental facility for the internet of things. This project aims to establish a large-scale, real-world experimental facility for the Internet of Things (IoT), which is currently missing in Australia, as well as in the rest of the world. The project is expected to be an essential instrument to achieve Australia’s leadership on key enabling technologies of the IoT, and to provide Australian research community with a unique platform for large-scale experimentation and ....A large-scale distributed experimental facility for the internet of things. This project aims to establish a large-scale, real-world experimental facility for the Internet of Things (IoT), which is currently missing in Australia, as well as in the rest of the world. The project is expected to be an essential instrument to achieve Australia’s leadership on key enabling technologies of the IoT, and to provide Australian research community with a unique platform for large-scale experimentation and evaluation of IoT technologies and services. The project will also serve as a vehicle for the education and training of Australia’s next generation of scholars and engineers, and contribute to Australia’s scientific visibility.Read moreRead less
Contextual Behabiour Predictions in Dynamic Mobile E-commerce. The project aims to address behaviour prediction and develop novel techniques and tools for modelling, predicting human behaviours and making effective recommendations based on ubiquitous user behaviour data in mobile e-commerce. The techniques enable multi-source data fusion, context learning and model adaptation, and dynamic recommendation with interpretability ability. Expected outcomes include advances in data analytics theory an ....Contextual Behabiour Predictions in Dynamic Mobile E-commerce. The project aims to address behaviour prediction and develop novel techniques and tools for modelling, predicting human behaviours and making effective recommendations based on ubiquitous user behaviour data in mobile e-commerce. The techniques enable multi-source data fusion, context learning and model adaptation, and dynamic recommendation with interpretability ability. Expected outcomes include advances in data analytics theory and informed decision-making. This provides significant benefits of not only placing Australia in the forefront of exploiting multimodal user behaviour big data in dynamic e-commerce but also transforming Australian government and businesses to intelligent and contextual services adaptive to complex situations.Read moreRead less
Efficient processing of large scale multi-dimensional graphs. This project aims to develop novel approaches to process large scale multi-dimensional graphs. The project will focus on the three most representative types of problems against multi-dimensional graphs, namely cohesive subgraph computation, frequent subgraph mining, and subgraph matching. The project outcome will include a set of new theories, novel indexing and data processing techniques, including distributed and single node computa ....Efficient processing of large scale multi-dimensional graphs. This project aims to develop novel approaches to process large scale multi-dimensional graphs. The project will focus on the three most representative types of problems against multi-dimensional graphs, namely cohesive subgraph computation, frequent subgraph mining, and subgraph matching. The project outcome will include a set of new theories, novel indexing and data processing techniques, including distributed and single node computation. The success of the project will significantly contribute to the technology development and the scientific foundation of big graph processing.Read moreRead less
Towards efficient processing of big graphs. This project aims to develop theory and techniques for efficient and scalable processing of Big Graph, a major field in Big Data. The project will focus on primitive graph queries covering many applications. Anticipated outcomes include a set of theories, indexing and data processing (including distributed and approximate) techniques. The success of the project is expected to contribute to the technology development and the scientific foundation of Big ....Towards efficient processing of big graphs. This project aims to develop theory and techniques for efficient and scalable processing of Big Graph, a major field in Big Data. The project will focus on primitive graph queries covering many applications. Anticipated outcomes include a set of theories, indexing and data processing (including distributed and approximate) techniques. The success of the project is expected to contribute to the technology development and the scientific foundation of Big Graph processing.Read moreRead less