Mining Distributed High-Speed Time-Variant Data Streams (2005–2007)

In the current computing environment different types of networks are connected to data sources. Data content is often hidden in data streams rather than in static data collections. The mining of dynamically changing time variant data streams presents new challenges. There is an expectation to deliver accurate knowledge discovery in an efficient way in order to provide instant decision support. This project aims to investigate selected aspects of the new generation of data mining problems; consider maintenance-based methods for rules and patterns discovery provide a comparative study on different solutions and investigate methods of data transformation that convert multiple distributed data streams into a data-mining-ready structure
Grant type:
ARC Discovery Projects
  • Professor
    School of Information Technology and Electrical Engineering
    Faculty of Engineering, Architecture and Information Technology
Funded by:
Australian Research Council