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.Read moreRead less
The Impact of Information about Data Quality on Decision Making. Data quality problems are widespread in practice and have significant economic impacts. The development of theoretically sound data quality tags and understanding how they impact decision outcomes and processes will lead to improved data quality management within Australian organisations and more efficient and effective decision making. These issues constitute an important area of information technology research. Outcomes from the ....The Impact of Information about Data Quality on Decision Making. Data quality problems are widespread in practice and have significant economic impacts. The development of theoretically sound data quality tags and understanding how they impact decision outcomes and processes will lead to improved data quality management within Australian organisations and more efficient and effective decision making. These issues constitute an important area of information technology research. Outcomes from the project will enhance Australia's research standing and contribute to university teaching and researcher training.Read moreRead less
Efficient Processing of Complex Spatial Queries. Similarity search and join are two of the most popular yet complex queiries in spatial databases. They are also two of the major spatial data analysis paradigms. To complement the existing techniques, this project aims to investigate a more complex and important form of these two problems, and to develop novel framework to approach the proposed problems. The successful achievements of the project will not only bring new spatial data analysis techn ....Efficient Processing of Complex Spatial Queries. Similarity search and join are two of the most popular yet complex queiries in spatial databases. They are also two of the major spatial data analysis paradigms. To complement the existing techniques, this project aims to investigate a more complex and important form of these two problems, and to develop novel framework to approach the proposed problems. The successful achievements of the project will not only bring new spatial data analysis techniques but also deliever effective solutions to a number of real-life apllications.Read moreRead less
Data Enhancement, Integration and Access Services for Smarter, Collaborative and Adaptive Whole-of Water Cycle Management. The project provides a valuable opportunity to make significant impact on water resource management and create community partnerships that will go well beyond the lifetime of the project. The project is expected to contribute to improved water quality and healthier ecosystems. In turn, the scientifically rich research environment will benefit all involved. It will demonstrat ....Data Enhancement, Integration and Access Services for Smarter, Collaborative and Adaptive Whole-of Water Cycle Management. The project provides a valuable opportunity to make significant impact on water resource management and create community partnerships that will go well beyond the lifetime of the project. The project is expected to contribute to improved water quality and healthier ecosystems. In turn, the scientifically rich research environment will benefit all involved. It will demonstrate the capability of the Australian researchers in addressing complex problems in data integration and quality. In particular there will be far reaching benefits of research training for associated PhD students and staff.Read moreRead less
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.Read moreRead less
Making sense of trajectory data: a database approach. This project investigates new challenges related to providing functionality, flexibility and efficiency for large scale trajectory data management and processing. The expected outcome includes significant technical contributions in novel indexing structures and advanced query processing methods for making better use of rich trajectory data.
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.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101610
Funder
Australian Research Council
Funding Amount
$403,398.00
Summary
Towards Explainable Multi-source Multivariate Time-series Analysis. The aim of this project is to build deep learning models with transparent reasoning behind the results that can be easily interpreted by humans. The research rests on translating pertinent knowledge from multiple sources of complex data containing event sequences into graph form and embedding those knowledge graphs into a sophisticated deep learning model. Such an accomplishment represents the next great advance in machine intel ....Towards Explainable Multi-source Multivariate Time-series Analysis. The aim of this project is to build deep learning models with transparent reasoning behind the results that can be easily interpreted by humans. The research rests on translating pertinent knowledge from multiple sources of complex data containing event sequences into graph form and embedding those knowledge graphs into a sophisticated deep learning model. Such an accomplishment represents the next great advance in machine intelligence and will lay the theoretical foundations for building intelligent analysis tools that truly work in tandem with people. The potential benefits to science, society, and the Australian economy, particularly in finance, sensor technologies, and emergency health services would be appreciable.Read moreRead less