Mapster: A Peer-to-Peer Data Sharing Environment

This paper describes a system called Mapster that allows users in a P2P network to share their databases. The research addresses problems of heterogeneity and scalability in P2P databases. To provide fine-grained access to users’ databases, schema matching and a super-peer topology are used. The schema matching component allows information to be translated by semi-automatically determining the mappings between the databases within the P2P network. A super-peer topology enables the schema matching techniques to operate effectively in large, dynamic, heterogeneous networks

Developing a Basis for Knowledge Management: A Bayesian Network Approach

Knowledge Management (KM) is an evolving field that attempts to maximise and sustain the competitive advantage of a company through leveraging its knowledge resources. KM practises are often built on a foundation of knowledge transfer and knowledge sharing. Recently there has been an increase on the reliance of automated tools to perform these functions. Typical components of these tools include: querying large datasets, user profiling, user interfaces and recommender systems. Traditionally, these components have been implemented using different technologies. This paper describes an approach to building these components using a flexible architecture based on Bayesian Network technology. Finally the paper considers some of the advantages to adopting the latter approach