Researh Projects
Towards Processing of Big Streaming Temporal Graphs
2024 - 2026, Dr Dong Wen, $435,000
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.
Next-Generation Distributed Graph Engine for Big Graphs
2024, Prof Lu Qin, Prof Ying Zhang and Dr Xiaoyang Wang, $508,000
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.
Big temporal graph processing in the Cloud
2023 - 2026, Prof Wenjie Zhang, Prof Ying Zhang, Dr Dong Wen and Dr Xiaoyang Wang, $495,000
Temporal graphs' are powerful tools to expressively model time-evolving relationships among different entities, underpinning a wide spectrum of applications from e-commerce to public health. The volume of temporal graph data in real-world applications can be very large. However, existing techniques for temporal graph processing mainly focus on single-machine solutions. This project will bridge this important gap by developing novel techniques for scalable and efficient temporal graph processing in the cloud. The success of this project will bring technological advances in the processing of big graphs, positioning Australia as a leader of the research field of graph processing and analytics. Through partnership and the licensing of IP, the enhanced graph processing capability from this project will deliver significant commercial and social benefits for key Australian industry sectors, including financial frauds detection in e-commerce, network attacks and malware detection in cyber-security systems, contact tracing in public health, and terrorist detection from social network analysis in defence.
Efficient and Scalable Processing of Dynamic Heterogeneous Graphs
2022 - 2026, Prof Wenjie Zhang, $1,085,000
This project will develop effective and innovative solutions for large-volume dynamic heterogeneous graph processing, which is in high demand for a broad spectrum of application in Australia. The success of this project will bring breakthroughs in technological advances in the processing of large-scale dynamic heterogeneous graphs including new theories, novel indexing, scalable processing techniques, complexity analysis and system development. This will ensure Australia to take a leadership and be in the forefront of this important research field. The project also has a great value to the development of local industry including cybersecurity systems to detect network intrusion and malware, e-commerce systems to detect financial fraud and predict customer preferences, health to identify useful functional structures in drug discovery, and social network to identify potential terrorists. The project will also facilitate the training of national most wanted IT professional talents.
Structure Search Over Large Scale Heterogeneous Information Networks
2020 - 2022, Xuemin Lin, $540,000
This project aims to develop novel mathematical frameworks for probabilistic geophysical imaging and inference, building on recent advances in statistics and machine learning. These will allow us to obtain a more detailed and robust understanding of structures and processes occurring within the Earth, including those relevant to the Australian minerals and/or energy industries. Outcomes of this research include mathematical and computational tools for imaging the subsurface, and greater understanding of Australian and global geoscience. This work can permit more effective exploitation of earth resources, as well as improving our understanding of how the Earth system has developed over geological history.
Cohesive Subgraph Discovery on Big Bipartite Graphs
2020 - 2022, Wenjie Zhang, $430,000
This project aims to develop novel technology for efficient and scalable cohesive subgraph discovery on big bipartite graphs, including new theories, indexing techniques, and data processing algorithms. We anticipate addressing key challenges and laying scientific foundations of big graph computation, as well as delivering high-impact technologies. The success of the project will directly benefit the key applications in Australia such as cyber-security, health, bio-informatics, social networks, and E-commerce. The success of the project will also facilitate the training of PhD graduates and postdoctoral research associates in the area of Big Data.
Efficient Processing of Large Scale Multi-dimensional Graphs
2018 - 2020, Xuemin Lin, Wenjie Zhang and Ying Zhang, $407,974
This project aims to develop novel approaches to process large scale multi-dimensional graphs. The project will focus on the three most representative types of problems against multi-dimensional graphs, namely cohesive subgraph computation, frequent subgraph mining, and subgraph matching. The project outcome will include a set of new theories, novel indexing and data processing techniques, including distributed and single node computation. The success of the project will significantly contribute to the technology development and the scientific foundation of big graph processing.