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Taming Large-Volume Dynamic Graphs in the Cloud

Funding: 2021: $220,000
2022: $220,000
2023: $205,551
2024: $205,551

Project Member(s): Qin, L.

Funding or Partner Organisation: Australian Research Council (ARC Future Fellowships)
Australian Research Council (ARC Future Fellowships)

Start year: 2021

Summary: This project aims to develop efficient and scalable algorithms to process dynamic big graphs in the cloud. The project will address key challenges and lay theoretical foundations in dynamic big graph processing which plays an important role in developing general-purpose, real-time structural search engines. The project will deliver substantial outcomes including theoretical foundations and scalable algorithms to process, in the cloud, big graphs that evolve rapidly over time. These will enable users to monitor and analyse structural information in large dynamic networks in real time. The project will open up a new research direction for graph processing to enrich frontier technologies and also benefit many key applications in Australia.


Yang, P, Wang, H, Lian, D, Zhang, Y, Qin, L & Zhang, W 1970, 'TMN: Trajectory Matching Networks for Predicting Similarity.', ICDE, 2022 IEEE 38th International Conference on Data Engineering (ICDE), IEEE, pp. 1700-1713.
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FOR Codes: Database Management, Information Processing Services (incl. Data Entry and Capture), Electronic Information Storage and Retrieval Services, Expanding Knowledge in the Information and Computing Sciences, Database systems, Information systems, technologies and services not elsewhere classified