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Trust-Oriented Data Analytics in Online Social Networks. Trust-oriented data analytics is essential in online social networks for reducing deceitful interactions and enhancing trust between users. This project aims to systematically devise innovative solutions by considering rich social contextual information as an important source of trust. The expected outcomes of this project include innovative solutions from a fundamental perspective to the challenges of context-aware trust propagation, trus ....Trust-Oriented Data Analytics in Online Social Networks. Trust-oriented data analytics is essential in online social networks for reducing deceitful interactions and enhancing trust between users. This project aims to systematically devise innovative solutions by considering rich social contextual information as an important source of trust. The expected outcomes of this project include innovative solutions from a fundamental perspective to the challenges of context-aware trust propagation, trust network searching/matching, and trustworthy/malicious user prediction in online social networks. This project is significant as it will advance the knowledge base for enabling a trustworthy social networking environment, benefiting billions of Australian and worldwide online social network users.Read moreRead less
Scaling Disk-Resident Learned Indexes For Database Systems. This project aims to investigate new disk-resident learned indexing algorithms to store and process data in database systems by advancing the state-of-the-art in memory-resident learned modeling. This project expects to generate new knowledge in the area of digital storage technologies utilising novel and efficient techniques in learned indexing for big data. This should provide significant benefits to enable modern database systems to ....Scaling Disk-Resident Learned Indexes For Database Systems. This project aims to investigate new disk-resident learned indexing algorithms to store and process data in database systems by advancing the state-of-the-art in memory-resident learned modeling. This project expects to generate new knowledge in the area of digital storage technologies utilising novel and efficient techniques in learned indexing for big data. This should provide significant benefits to enable modern database systems to scale with the massive growth of data, improve the efficiency of data processing, improve the effectiveness of projects that utilise big data, and dramatically reduce energy costs in Australian data centres when storing and retrieving data from databases and lower their carbon footprints.Read moreRead less
Differential Evolution Framework for Intelligent Charging Scheduling. Smart charging scheduling is a vital challenge as dynamic environment with traffic networks and various unexpected issues. This project aims to develop a differential evolution framework for intelligent charging scheduling. The framework consists of a comprehensive charging scheduling model with various road networks and factors. The project outcomes include a distributed evolutionary computation framework, differential evolut ....Differential Evolution Framework for Intelligent Charging Scheduling. Smart charging scheduling is a vital challenge as dynamic environment with traffic networks and various unexpected issues. This project aims to develop a differential evolution framework for intelligent charging scheduling. The framework consists of a comprehensive charging scheduling model with various road networks and factors. The project outcomes include a distributed evolutionary computation framework, differential evolution algorithms, and cooperative co-evolutionary strategies. The outcome results will be demonstrated by practical evaluations over public datasets and comparisons to related works. The project is beneficial to the nation in both theory of artificial intelligence techniques and applications of real transport systems.Read moreRead less
Big temporal graph processing in the Cloud. This project aims to develop efficient and scalable algorithms to process big temporal graphs in the Cloud. In particular, we will investigate three most representative types of queries over big temporal graphs including vertex-based queries, path-based queries, and subgraph-based queries. Expected outcomes of this project include theoretical foundations and scalable algorithms to process big temporal graphs as well as a system prototype for evaluation ....Big temporal graph processing in the Cloud. This project aims to develop efficient and scalable algorithms to process big temporal graphs in the Cloud. In particular, we will investigate three most representative types of queries over big temporal graphs including vertex-based queries, path-based queries, and subgraph-based queries. Expected outcomes of this project include theoretical foundations and scalable algorithms to process big temporal graphs as well as a system prototype for evaluation and to demonstrate the practical value. Success in this project should see significant benefits for many important applications such as cybersecurity, e-commerce, health and road networks.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100668
Funder
Australian Research Council
Funding Amount
$435,000.00
Summary
Towards Processing of Big Streaming Temporal Graphs. This project aims to develop efficient and scalable algorithms to process big streaming temporal graphs, which is in high demand for many data-intensive applications such as cybersecurity, crime monitoring, and e-marketing. In particular, I will investigate three most representative types of queries including vertex-based queries, path-based queries, and subgraph-based queries. Expected outcomes of this project include theoretical foundations ....Towards Processing of Big Streaming Temporal Graphs. This project aims to develop efficient and scalable algorithms to process big streaming temporal graphs, which is in high demand for many data-intensive applications such as cybersecurity, crime monitoring, and e-marketing. In particular, I will investigate three most representative types of queries including vertex-based queries, path-based queries, and subgraph-based queries. Expected outcomes of this project include theoretical foundations and scalable algorithms to process big streaming temporal graphs as well as a system prototype for evaluation and to demonstrate the practical value. Success in this project should see significant benefits for many important applications such as cybersecurity, e-commerce, health and social analysis.Read moreRead less
Next-Generation Distributed Graph Engine for Big Graphs. This project aims to develop an efficient and scalable distributed graph engine to process big graphs. In particular, we will investigate the foundations for the distributed real-time graph engine, focusing on graph storage and graph operators, and then provide solutions for a set of representative graph mining and query processing tasks. Expected outcomes of this project include theoretical foundations and a scalable real-time graph engin ....Next-Generation Distributed Graph Engine for Big Graphs. This project aims to develop an efficient and scalable distributed graph engine to process big graphs. In particular, we will investigate the foundations for the distributed real-time graph engine, focusing on graph storage and graph operators, and then provide solutions for a set of representative graph mining and query processing tasks. Expected outcomes of this project include theoretical foundations and a scalable real-time graph engine to process big graphs as well as a system prototype for evaluation and to demonstrate the practical value. Success in this project should see significant benefits for many important applications such as cybersecurity, e-commerce, health and road networks.Read moreRead less
Constraints in XML Schema Integration. This project will produce worldwide leading technologies for designing XML data integration system. With the technologies, the well designed integration systems will be able store data with rich semantics and thus provide accurate and understandable information to users. In this way, Australia and communities will be benefited both financially and informatively. The research of this project will also add to the research reputation of Australia in data integ ....Constraints in XML Schema Integration. This project will produce worldwide leading technologies for designing XML data integration system. With the technologies, the well designed integration systems will be able store data with rich semantics and thus provide accurate and understandable information to users. In this way, Australia and communities will be benefited both financially and informatively. The research of this project will also add to the research reputation of Australia in data integration areas. At the same time, the knowledge capacity of Australia on data integration will be enlarged which further improves frontier research activities in the area. Through the research of the project, PhD students will be trained.Read moreRead less
Privacy-preserving cloud data mining-as-a-service. This project aims to explore practical privacy-preserving solutions for cloud data mining-as-a-service based on the Intel Software Guard Extensions (SGX) technology. The research addresses privacy concerns of users when outsourcing data mining needs to the cloud. These concerns have increased as more businesses evaluate data mining-as-an outsourced service due to lack of expertise or computation resources. The expected outcomes from the research ....Privacy-preserving cloud data mining-as-a-service. This project aims to explore practical privacy-preserving solutions for cloud data mining-as-a-service based on the Intel Software Guard Extensions (SGX) technology. The research addresses privacy concerns of users when outsourcing data mining needs to the cloud. These concerns have increased as more businesses evaluate data mining-as-an outsourced service due to lack of expertise or computation resources. The expected outcomes from the research will include new data privacy models, new privacy-preserving data mining algorithms, and a prototype of cloud data mining software. These will help businesses cut costs for data mining and privacy protection, and provide significant benefits toward helping Australia achieve its national cyber security strategy and potentially provide economic impact from commercialisation of new software technology for the industry partner.Read moreRead less
XML Views of Relational Databases: Semantics and Update Problems. XML is the standard for representing, publishing and exchanging data over the Internet and relational database is the dominant technology for data management. Updating XML views over relational data is fundamental to bring these two technologies together to serve Internet-based applications. Australia has been a leading country in both developing and applying internet technologies. The theoretic outcomes of this project will contr ....XML Views of Relational Databases: Semantics and Update Problems. XML is the standard for representing, publishing and exchanging data over the Internet and relational database is the dominant technology for data management. Updating XML views over relational data is fundamental to bring these two technologies together to serve Internet-based applications. Australia has been a leading country in both developing and applying internet technologies. The theoretic outcomes of this project will contribute to the advance in database and web research communities and establish us as an internationally leading group in this research area. The technological outcomes will help organisations in Australia effectively and efficiently conduct e-Business on the Internet. Read moreRead less
Accurate Context-Aware Search for Managed Document Collections. Search is a key component of a vast range of computing applications. However, while search on large collections of general-purpose text is well-understood, and the best systems are highly effective, search on smaller or special-purpose collections is much less reliable and has attracted relatively little research. By identifying general ways of using context such as user history or prior page usefulness, the quality of search in s ....Accurate Context-Aware Search for Managed Document Collections. Search is a key component of a vast range of computing applications. However, while search on large collections of general-purpose text is well-understood, and the best systems are highly effective, search on smaller or special-purpose collections is much less reliable and has attracted relatively little research. By identifying general ways of using context such as user history or prior page usefulness, the quality of search in such cases can be greatly improved. Products that make use of these principles will provide greater workplace efficiency and be able to locate information that other search tools cannot identify.Read moreRead less