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Field of Research : Database Management
Research Topic : Decision support techniques
Australian State/Territory : SA
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  • Funded Activity

    Discovery Projects - Grant ID: DP0346054

    Funder
    Australian Research Council
    Funding Amount
    $155,000.00
    Summary
    Continuous process improvement through workstation feedback for General Practice medicine using experts-in-the-loop data mining. This project investigates the iterative use of data mining results to allow experts to construct feedback to influence subsequent production work. We explore the problem in the context of General Practice medicine by having General Practitioners (GPs) review emerging patterns from their own practice's electronic medical records and author feedback to discourage undesi .... Continuous process improvement through workstation feedback for General Practice medicine using experts-in-the-loop data mining. This project investigates the iterative use of data mining results to allow experts to construct feedback to influence subsequent production work. We explore the problem in the context of General Practice medicine by having General Practitioners (GPs) review emerging patterns from their own practice's electronic medical records and author feedback to discourage undesirable patterns. The work will have immediate applicability to medical practice and will drive innovation in data mining method, notably for efficient identification of temporal and complex niche patterns. More broadly, the work will extend the way data mining is used to create new expectations of workstation behaviour.
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    Funded Activity

    Special Research Initiatives - Grant ID: SR0354744

    Funder
    Australian Research Council
    Funding Amount
    $20,000.00
    Summary
    Improving Australia's Data Mining and Knowledge Discovery Research. The network will bring together over 50 active researchers in data mining and knowledge discovery to enhance and better coordinate Australia's impressive research performance in these dual disciplines. Specifically, the network will (a) facilitate communication and collaboration between researchers, (b) fund or underwrite opportunities for international collaboration, (c) run a number of specialist workshops and symposia and (d .... Improving Australia's Data Mining and Knowledge Discovery Research. The network will bring together over 50 active researchers in data mining and knowledge discovery to enhance and better coordinate Australia's impressive research performance in these dual disciplines. Specifically, the network will (a) facilitate communication and collaboration between researchers, (b) fund or underwrite opportunities for international collaboration, (c) run a number of specialist workshops and symposia and (d) establish a national annual conference.
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    Funded Activity

    Discovery Projects - Grant ID: DP140100104

    Funder
    Australian Research Council
    Funding Amount
    $411,000.00
    Summary
    Effective Recommendations based on Multi-Source Data. Large-scale data collected from multiple sources such as the Web, sensor networks, academic publications, and social networks provide a new opportunity to exploit useful information for effective and efficient recommendations and decision making. The project will propose a new framework of recommender systems that is based on analysing relationships between different types of objects from multiple data sources. A graph model will be built to .... Effective Recommendations based on Multi-Source Data. Large-scale data collected from multiple sources such as the Web, sensor networks, academic publications, and social networks provide a new opportunity to exploit useful information for effective and efficient recommendations and decision making. The project will propose a new framework of recommender systems that is based on analysing relationships between different types of objects from multiple data sources. A graph model will be built to represent the extracted semantic relationships and novel linkage-analysis based algorithms will be developed for ranking objects. The results from this project will underpin many critical applications such as healthcare.
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