Agents and stream data mining: a new perspective

Many organizations struggle with the massive amount of data they collect. Today, data does more than serve as the ingredients for churning out statistical reports. They help support efficient operations in many organizations, and to some extent, data provide the competitive intelligence organizations need to survive in today's economy. Data mining can't always deliver timely and relevant results because data are constantly changing. However, stream-data processing might be more effective, judging by the Matrix project.

An analysis of the environment as a means of assessing training needs in the Insurance industry in Australia

In the five years leading up to 2002 there were many significant changes in
the insurance industry in Australia that brought about a range of training
needs. These training needs arose from matters as diverse as mergers, increased competition, corporate failures, and legislative changes. This study includes findings from a survey of the insurance industry in Australia in the period 2000·2002 asa means of exploring the importance of the environment (marketplace) in predicting aspects of training needs. The findings demonstrate that an environmental analysis approach to training needs analysis can predict the type of training that organisations will subsequently need to provide, and thus has the potential to produce a more accurate assessment of training needs in the future. The findings also have application to more broadly based businesses operating in the financial services sector of the economy

Integrating insurance services, trust and risk mechanisms into multi-agent systems

In multi-agent systems, there is often the need for an agent to cooperate with others so as to ensure that a given task is achieved timely and cost effectively. Currently multi-agent systems maximize this through mechanisms such as coalition formation, trust and risk assessments, etc. In this paper, we incorporate the concept of insurance with trust and risk mechanisms in multi-agent systems. The novelty of this proposal is that it ensures continuous sharing of resources while encouraging expected utility to be maximized in a dynamic environment. Our experimental results confirm the feasibility of our approach.