Discovery Early Career Researcher Award - Grant ID: DE210101808
Funder
Australian Research Council
Funding Amount
$395,775.00
Summary
Genetic Programming for Big Data Analytics. The project aims to extend a powerful machine learning method, called genetic programming and also developing a new concept called Alpha program, for big data analytics. This project expects to generate a new approach by finding a systematic approach to develop gene structures using information theory. By borrowing the best genes from the population of programs, the Alpha program concept will be developed for the first time. The proposed approach aims ....Genetic Programming for Big Data Analytics. The project aims to extend a powerful machine learning method, called genetic programming and also developing a new concept called Alpha program, for big data analytics. This project expects to generate a new approach by finding a systematic approach to develop gene structures using information theory. By borrowing the best genes from the population of programs, the Alpha program concept will be developed for the first time. The proposed approach aims to enhance genetic programming for many practical problems. I contend that not only finding better tools for big data analytics is in the best interest of machine learning and big data communities, it also provides significant benefits for other communities and industries in Australia.Read moreRead less
Approximate structures for efficient processing of data streams. This project aims to increase the volume of streamed data that can be handled on a low-powered device with limited memory. In finance, health, and transport, data arrives at enormous rates, and data-driven decisions must be made quickly. Likewise, to keep Australia secure, national agencies monitor and gather vast data sets. Increasingly, devices and monitors that have limited resources are making these decisions and they require c ....Approximate structures for efficient processing of data streams. This project aims to increase the volume of streamed data that can be handled on a low-powered device with limited memory. In finance, health, and transport, data arrives at enormous rates, and data-driven decisions must be made quickly. Likewise, to keep Australia secure, national agencies monitor and gather vast data sets. Increasingly, devices and monitors that have limited resources are making these decisions and they require computational techniques that run extremely efficiently. The project expects to develop and improve approximate data structures that operate in tight resource bounds. Anticipated outcomes are improved event recognition and dramatic speedup in analysis of streams in areas such as finance, health, transport, and urban data.Read moreRead less
Searching Cohesive Subgraphs in Big Attributed Graph Data. The availability of big attributed graph data brings great opportunities for realizing big values of data. Making sense of such big attributed graph data finds many applications, including health, science, engineering, business, environment, etc. A cohesive subgraph, one of key components that captures the latent properties in a graph, is essential to graph analysis. This project aims to invent effective models of cohesive subgraphs and ....Searching Cohesive Subgraphs in Big Attributed Graph Data. The availability of big attributed graph data brings great opportunities for realizing big values of data. Making sense of such big attributed graph data finds many applications, including health, science, engineering, business, environment, etc. A cohesive subgraph, one of key components that captures the latent properties in a graph, is essential to graph analysis. This project aims to invent effective models of cohesive subgraphs and efficient algorithms for searching and monitoring cohesive subgraphs in big and dynamic attributed graphs from both structure and attribute perspectives. The methods, techniques, and prototype systems developed in this project can be deployed to facilitate the smart use of big graph data across the nation. Read moreRead less
Making Spatiotemporal Data More Useful: An Entity Linking Approach. This project aims to establish a methodology for spatiotemporal entity linking by utilising object movement traces to support database integration and data quality management for the next-generation of data where spatiotemporal attributes are ubiquitous. It expects to develop a novel entity linking paradigm for automatic, efficient and reliable spatiotemporal data integration together with a new data privacy study in this contex ....Making Spatiotemporal Data More Useful: An Entity Linking Approach. This project aims to establish a methodology for spatiotemporal entity linking by utilising object movement traces to support database integration and data quality management for the next-generation of data where spatiotemporal attributes are ubiquitous. It expects to develop a novel entity linking paradigm for automatic, efficient and reliable spatiotemporal data integration together with a new data privacy study in this context. Expected outcome include new database technologies for data signature generation and similarity-based search, and improved location data privacy protection methods. This project should provide significant benefits to all areas where high quality spatiotemporal data fusion is essential to meaningful data analysis.Read moreRead less
New approaches to interactive sessional search for complex tasks. This project aims to develop new tools and techniques to improve the accuracy and speed of search and data analytics for complex information tasks. There are currently no publicly available search engines which support users engaged in complex interactive search, or that allow searchers to fully control their own data and privacy. Fundamental research advances, based on understanding real user behaviour and search needs will have ....New approaches to interactive sessional search for complex tasks. This project aims to develop new tools and techniques to improve the accuracy and speed of search and data analytics for complex information tasks. There are currently no publicly available search engines which support users engaged in complex interactive search, or that allow searchers to fully control their own data and privacy. Fundamental research advances, based on understanding real user behaviour and search needs will have an impact on important academic, industrial, and government domains, including virtual assistants, health care (clinical decision support), precision medicine, eDiscovery, crime prevention, and detailed socio-economic evaluations.Read moreRead less
Modelling and Searching Cohesive Groups over Heterogeneous Graphs . Heterogeneous information networks (HINs) contain richer structural and semantic information represented as different types of objects and links. Searching cohesive groups from HINs finds many applications and also brings challenges at both conceptual and technical levels. This project aims to investigate the effective modelling of cohesive groups that take both homogeneous and heterogeneous information into account for differen ....Modelling and Searching Cohesive Groups over Heterogeneous Graphs . Heterogeneous information networks (HINs) contain richer structural and semantic information represented as different types of objects and links. Searching cohesive groups from HINs finds many applications and also brings challenges at both conceptual and technical levels. This project aims to investigate the effective modelling of cohesive groups that take both homogeneous and heterogeneous information into account for different applications and devise efficient algorithms for searching and monitoring those cohesive groups based on different models. The methods, techniques, and evaluation systems developed in this project can be deployed to facilitate the smart use of heterogeneous information networks across the nation.Read moreRead less
Adaptive Key-value Store for Future Extreme Heterogeneous Systems. Safe, lasting storage of data, and efficient access to it, is vital for all aspects of computing, ranging from e-commerce applications, and data-management in governments. For the storage of data, persistent key-value stores are central in modern computing platforms. However, contemporary key-value stores have not been designed for emerging extreme heterogeneous computational systems with future hardware accelerators and storage ....Adaptive Key-value Store for Future Extreme Heterogeneous Systems. Safe, lasting storage of data, and efficient access to it, is vital for all aspects of computing, ranging from e-commerce applications, and data-management in governments. For the storage of data, persistent key-value stores are central in modern computing platforms. However, contemporary key-value stores have not been designed for emerging extreme heterogeneous computational systems with future hardware accelerators and storage capabilities, including graphics processor and flash-based memory. This project will devise an adaptive key-value store framework for heterogeneous systems. Our new framework will adaptively harvest the performance potential of future hardware such that applications can cope with fast-growing data sets.Read moreRead less