Knowledge Management Group

This group has a number of projects running:

This project focuses on building a formal model that can be used to model interaction of semantic web components to show that the construction and execution of composite web services can be automated.

XML semantic query optimization is the method that optimizes the XML query execution based on the semantic information, which is abstracted from the XML document. In our research, elements in an XML document will be classified based on the value distributions of their sub elements.

Data warehouses are non-volatile data repositories that play a vital role in making business decisions by analyzing the operational data. Real-time data warehouses focus as increasing data freshness levels. The tools and techniques for increasing this freshness level are therefore rapidly evolving. Extract-Transform-Load (ETL) tools feed data from operational databases into data warehouses. Traditionally, these ETL tools use batch processing and operate offline at regular time intervals, for example on a nightly or weekly basis. Naturally, users prefer to have up-to-date data to make their decisions, therefore there is a demand for real-time ETL tools. The Data Transformation is an important phase in ETL where source data is transformed into warehouse format and necessary enrichment of Master Data is performed using join operator.

The research related to testing and quality assurance aspects of web services is not mature. This is especially true for semantic web services, since research to-date has mainly focused on the automation of WS tasks. Furthermore, some semantic web service frameworks promote client-oriented SOA, by formally specifying user requirements, called "goal specification", and automatically resolve it by appropriate web service detection.

The research includes the in-depth study of the following areas: develop an accurate and efficient model for Particle Swarm Optimization (PSO) based data clustering, a study the behaviour of PSO based clustering techniques for Hierarchical agglomerative clustering (HAC), performing outlier detection using particle swarm optimization technique, and combining these 3 sub-projects to develop an intelligent recommendation system.