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Multi-Stream Drift for Real-Time Decision Support

Project Member(s): Zhang, G., Xuan, J.

Funding or Partner Organisation: Australian Research Council (ARC Discovery Projects)
Australian Research Council (ARC Discovery Projects)

Start year: 2019

Summary: Drift learning for decision-making in dynamic multi-stream environments. This project aims to provide application-ready real-time decision support systems for big data situations. Real-time support for organisational decisions is crucial in fast-changing environments that are highly dependent on data from multiple large streams. Unforeseen changes in data distribution (drift) are inevitable. The ability to learn drift in dynamic environments with multiple large data streams will benefit innovation and decision quality in challenging data situations. The project will have wide applications, such as in cybersecurity, telecommunications, bushfire control and logistics. The project will advance machine learning knowledge, providing a foundation and technologies to support real-time decision-making in big data environments.


Yin, R, Li, K, Lu, J & Zhang, G 1970, 'Enhancing Fashion Recommendation with Visual Compatibility Relationship', The World Wide Web Conference, WWW '19: The Web Conference, ACM, San Francisco CA USA, pp. 3434-3440.
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FOR Codes: Information Processing Services (incl. Data Entry and Capture), Application Software Packages (excl. Computer Games), Expanding Knowledge in the Information and Computing Sciences, Pattern Recognition and Data Mining, Decision Support and Group Support Systems, Neural, Evolutionary and Fuzzy Computation, Data mining and knowledge discovery, Evolutionary computation, Application software packages, Information systems, technologies and services not elsewhere classified