- Associate Professor Richi Nayak
- Associate Professor
Science and Engineering Faculty,
Electrical Engineering, Computer Science,
- Discipline *
- Artificial Intelligence and Image Processing, Computation Theory and Mathematics, Library and Information Studies
- +61 7 3138 1976
- View location details (QUT staff and student access only)
PhD (Queensland University of Technology)
Artificial intelligence, Data mining & knowledge discovery in databases, Machine learning techniques, Various application of data mining, Web mining and Web Intelligence, XML documents integration & mining, Machine Learning for Information Retrieval
Personalisation of Web Services using Link and Network Mining and Product Recommendation
This project will develop techniques and tools to better identify customer behaviour and need, and to apply these tools and techniques directly to improved products and services. This project will develop novel data mining techniques for user profiling and market segmentation. This will also develop recommendation and information filtering techniques for improving the relevance of content in services such as personalised search and matching.
Semi-structured Data Mining
XML is a standard for information exchange and retrieval. It has the potential to dramatically improve the quality of information sharing. This project aims to develop an innovative XML knowledge discovery framework that includes structure and content extraction, data integration, mining and querying. This project develops scalable clustering and frequent tree mining method that exploits the semantic structural and hierarchical nature of XML documents.
Applications of data mining into solving real-world problems
Developing real?time data mining systems by utilising the hidden patterns and rules behind the complex sets of data sets such as Road environmental and accidents data set; Anaesthetic time series data set; Active ageing survey dataset and Structural health monitoring.
Web service Discovery and Recommendation
With organizations implementing service-oriented architecture for daily transactions in business-to-business and business-to-customer processes, a huge amount of effort is being put into making the discovery of services more accurate. This project develops an advanced Web service discovery method that performs match?making to find semantically similar Web services for a user query by using semantic enhanced models such as Latent Semantic Models and finds a composition of multiple inter?related Web services by mining the relationships between Web services.
Teaching discipline: Information Systems
For publications by this staff member, visit QUT ePrints, the University's research repository.