A data driven paradigm for service-oriented system engineering. This project aims to design and develop a data driven paradigm for service-oriented system engineering that allows system engineers and domain experts in different domains to build software systems easily in order to enable fast technology transfer within and across domain boundaries. This model integrates and automates a suite of efficient approaches for system structure determination, validation and recommendation based on keyword ....A data driven paradigm for service-oriented system engineering. This project aims to design and develop a data driven paradigm for service-oriented system engineering that allows system engineers and domain experts in different domains to build software systems easily in order to enable fast technology transfer within and across domain boundaries. This model integrates and automates a suite of efficient approaches for system structure determination, validation and recommendation based on keyword search, subgraph isomorphism and substructure query techniques. This project is expected to significantly accelerate the application of new technologies, for example, big data analytics and Internet of Things, in many of Australia's critical domains such as e-Health, smart cities, and cybersecurity.Read moreRead less
QualA-D: a quality aware query engine for next generation data integration systems. This project will address the growing diversity of the web/user community by developing new approaches for data integration that incorporate data quality requirements such as data currency, completeness and coverage. First-of-breed quality aware query system is expected to be developed that will assist in improving user experience and satisfaction.
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 and effective ad-hoc search using structured and unstructured geospatial information. Web search is a key enabling technology in the information age. However two technologies, ubiquitous mobile devices and massive structured data repositories such as those used to maintain social networking sites, are changing user expectations about how and what should be searched. A key challenge in the research community is how to integrate structured and unstructured information to improve the qual ....Efficient and effective ad-hoc search using structured and unstructured geospatial information. Web search is a key enabling technology in the information age. However two technologies, ubiquitous mobile devices and massive structured data repositories such as those used to maintain social networking sites, are changing user expectations about how and what should be searched. A key challenge in the research community is how to integrate structured and unstructured information to improve the quality of search. This project proposes new approaches to ranked retrieval for location-aware search. In particular, it presents a plan to combine state-of-the-art research from two domains: spatial keyword search in databases, and ad-hoc search in Information Retrieval to improve the quality of search results.Read moreRead less
What Can You Trust in the Large and Noisy Web? This project will develop innovative techniques to efficiently and effectively distill truthful information from the inherently unreliable and large-scale Web environment, where misinformation has been widely regarded as a grand challenge for the next decade. The results of this project will not only maintain Australia’s leadership in this frontier research area, but also support many important applications that safeguard Australian people and econo ....What Can You Trust in the Large and Noisy Web? This project will develop innovative techniques to efficiently and effectively distill truthful information from the inherently unreliable and large-scale Web environment, where misinformation has been widely regarded as a grand challenge for the next decade. The results of this project will not only maintain Australia’s leadership in this frontier research area, but also support many important applications that safeguard Australian people and economy such as emergency and disaster management and online healthcare. This project also serves as an excellent vehicle for the education and training of Australia’s next generation of scholars and engineers.Read moreRead less
Integrating Quality Control into Crowd-sourcing Services. This project aims to contribute to the scientific foundations of quality control in crowd-sourcing services. Crowd-sourcing services harness the wisdom of large communities to solve problems. Open projects (eg Linux, Wikipedia) have successfully used crowd sourcing. Increasingly, organisations and governments are using crowd-sourcing to improve products and services. However, there are significant risks due to lack of robust quality contr ....Integrating Quality Control into Crowd-sourcing Services. This project aims to contribute to the scientific foundations of quality control in crowd-sourcing services. Crowd-sourcing services harness the wisdom of large communities to solve problems. Open projects (eg Linux, Wikipedia) have successfully used crowd sourcing. Increasingly, organisations and governments are using crowd-sourcing to improve products and services. However, there are significant risks due to lack of robust quality control methods. The project aims to contribute to the first methodology and techniques for systematic provisioning of robust crowd-sourcing quality control mechanisms. It is expected that project outcomes would contribute to lifting productivity and economic growth through quality-aware crowd sourcing service technologies.Read moreRead less
Trajectory data processing: Spatial computing meets information retrieval. This project aims to develop multi-stage retrieval systems which leverage structured and unstructured data processing to efficiently and effectively search spatial, temporal and textual data collections. Search in heterogeneous data collections is an important research problem. In particular, spatial computing is a growth area as the quantity and quality of GPS data collected in multiple domains has significantly increase ....Trajectory data processing: Spatial computing meets information retrieval. This project aims to develop multi-stage retrieval systems which leverage structured and unstructured data processing to efficiently and effectively search spatial, temporal and textual data collections. Search in heterogeneous data collections is an important research problem. In particular, spatial computing is a growth area as the quantity and quality of GPS data collected in multiple domains has significantly increased in recent years. Possible benefits from research advances derived from this project include disaster/event recognition and monitoring, monitoring of endangered species, farming and agriculture to increase crop yields and reduce cost, and minimising fuel consumption and greenhouse-gas emissions.Read moreRead less
Federated Cloud Services Configuration and Orchestration. Cloud computing allows organisations to expand or contract their computing footprint based on existing demand. However, existing cloud delivery models support individual segregated and heterogeneous functionalities, which prevent effective coordinated combination of on-premise and off-premise applications, services, and resources. This project aims to significantly contribute to the scientific foundations for the model-driven and elastic ....Federated Cloud Services Configuration and Orchestration. Cloud computing allows organisations to expand or contract their computing footprint based on existing demand. However, existing cloud delivery models support individual segregated and heterogeneous functionalities, which prevent effective coordinated combination of on-premise and off-premise applications, services, and resources. This project aims to significantly contribute to the scientific foundations for the model-driven and elastic configuration and orchestration of resources over heterogeneous cloud services. The outcomes of the project aim to contribute to lifting productivity and economic growth through interoperable and elastic cloud service technologies as well as delivering appropriate skills for the new digital economy.Read moreRead less
Robust Preference Inference from Spatial-Temporal Interaction Networks. This project aims to develop innovative techniques for effectively and efficiently managing user preference profiles from less labelled, sparse and noisy interaction data. A unified novel learning framework along with a set of data analysis techniques are expected to be developed from this project, which will provide a non-intrusive way of conducting predictive analysis on user preference profiling via discovering human expl ....Robust Preference Inference from Spatial-Temporal Interaction Networks. This project aims to develop innovative techniques for effectively and efficiently managing user preference profiles from less labelled, sparse and noisy interaction data. A unified novel learning framework along with a set of data analysis techniques are expected to be developed from this project, which will provide a non-intrusive way of conducting predictive analysis on user preference profiling via discovering human explicit and implicit interest domains. The expected results of this application will not only maintain Australia's leadership in this frontier research area, but also support many important applications that safeguard Australian people and economy such as cyber security, healthcare, and e-Commerce.Read moreRead less
A painless approach to support efficient querying and mining of spatial data through smart transformations. This project will develop spatial data retrieval methods that are not only highly efficient but also easy to implement ('painless'). It will help businesses such as digital map providers, location based service providers and medical researchers quickly possess this key enabling technique for their large scale spatial querying and mining needs.