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Field of Research : Information Systems Development Methodologies
Research Topic : mab database
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Database Management (7)
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  • Researchers (20)
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  • Funded Activity

    Discovery Projects - Grant ID: DP0987900

    Funder
    Australian Research Council
    Funding Amount
    $270,000.00
    Summary
    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.
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    Funded Activity

    Linkage Projects - Grant ID: LP0212047

    Funder
    Australian Research Council
    Funding Amount
    $135,270.00
    Summary
    A novel Internet-based clinical trial service. The best way to test whether a clinical treatment intervention is effective is a randomised controlled trial (rct). Rcts are expensive, both in terms of resources and time. The internet may provide a method of conducting rcts which is efficent and provides access to those in remote communities. The purpose of this project is to assess the feasibility of developing a structure for conducting rcts using the internet.
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    Funded Activity

    Discovery Projects - Grant ID: DP0773563

    Funder
    Australian Research Council
    Funding Amount
    $162,000.00
    Summary
    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.
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    Active Funded Activity

    Discovery Early Career Researcher Award - Grant ID: DE200101465

    Funder
    Australian Research Council
    Funding Amount
    $419,498.00
    Summary
    Minimising Human Efforts to Fight Fake News and Restore the Public Trust. Our modern society is struggling with an unprecedented amount of online fake news, which is recently driven by misused artificial intelligence (AI) technologies. This project aims to build the first real-time system integrating algorithmic models and human validators to counter such falsehoods, especially those AI-fabricated false stories. This project expects to deliver a series of cost-effective and streaming methods emp .... Minimising Human Efforts to Fight Fake News and Restore the Public Trust. Our modern society is struggling with an unprecedented amount of online fake news, which is recently driven by misused artificial intelligence (AI) technologies. This project aims to build the first real-time system integrating algorithmic models and human validators to counter such falsehoods, especially those AI-fabricated false stories. This project expects to deliver a series of cost-effective and streaming methods empowering a Web-based observatory dashboard of fake news propagation. This achieves significant benefits for media organisations, governments, the public, and academia via timely alerts, data-journalism reports, and novel data visualisations of social media landscape to distinguish between legitimate and deceptive contents.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP190101985

    Funder
    Australian Research Council
    Funding Amount
    $350,000.00
    Summary
    Challenging big data for scalable, robust and real-time recommendations. With the advent of big data era, recommender systems are facing unprecedented challenges with respect to the four dimensions of big data: big volume, low veracity, high velocity and high variety. This project aims to develop a new generation of cost-effective techniques for scalable, robust and real-time recommendations utilising big data. This project aims to address these challenges to achieve scalable, robust and real-ti .... Challenging big data for scalable, robust and real-time recommendations. With the advent of big data era, recommender systems are facing unprecedented challenges with respect to the four dimensions of big data: big volume, low veracity, high velocity and high variety. This project aims to develop a new generation of cost-effective techniques for scalable, robust and real-time recommendations utilising big data. This project aims to address these challenges to achieve scalable, robust and real-time recommendations. This project will devise a series of cost-effective machine learning methods and schemes to deliver an end-to-end recommender framework. This project has the potential to significantly reduce the energy consumption of large-scale recommender systems as well as facilitating an increase in the use of recommendation applications for big data.
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    Funded Activity

    Discovery Projects - Grant ID: DP130102748

    Funder
    Australian Research Council
    Funding Amount
    $315,000.00
    Summary
    Big data fast response: real-time classification of big data stream. This project will provide a big data stream based data mining framework to build a real-time monitoring and decision making platform for business and government big data. By clever use of smart information, this breakthrough science will provide frontier technologies for managing and using huge data for social and economic benefits globally.
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    Funded Activity

    Discovery Projects - Grant ID: DP140102206

    Funder
    Australian Research Council
    Funding Amount
    $382,000.00
    Summary
    Enabling User-Centric Wisdom Engines for Big Information Network Search. Big information networks, for example social networks, are important for modern information systems, yet searching for useful information from huge networks is difficult because network structure and user relationships continuously evolve. This project will provide theoretical foundations for structural knowledge mining to enable user-centric wisdom search on big information networks. Expected outcomes are: real-world appli .... Enabling User-Centric Wisdom Engines for Big Information Network Search. Big information networks, for example social networks, are important for modern information systems, yet searching for useful information from huge networks is difficult because network structure and user relationships continuously evolve. This project will provide theoretical foundations for structural knowledge mining to enable user-centric wisdom search on big information networks. Expected outcomes are: real-world application platform to support information network analysis; theories for big network control, algorithms, and systematic solutions to enable user-centric knowledge search, including a new search engine for big information networks. By significantly improving IT, it will benefit Australian business, industry and the wider community.
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    Showing 1-7 of 7 Funded Activites

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