A framework for cost-effective distributed data mining in academic institutions using intelligent agents

The purpose of this paper is to illustrate the maximum utilization of available computing resources for the data mining activities in the academic institutions. Data mining playing a huge role in predicting future from various data bases which includes weather databases, financial data portals. Emerging disease information systems has been recognized by industries as an important area. This brings the opportunity of major revenues from applications such as business data warehousing, process control, and personalized on-line customer services over Internet and web. Distributed Data Mining (DDM) is expected to perform partial analysis of data at the client's place and then send the outcome as results to the server where it is sometimes required to be aggregated to the global result. The primary issues to be considered for DDM are accuracy, scalability, privacy and autonomy of data. These issues can be easily handled with the intelligent software agents for DDM, because of its autonomous, adaptive and deliberative reasoning features. Apart from this, the positive side of this approach is that the complete system can be built using open-source environment such as Java Runtime Environment (JRE) which is a cost-effective approach and use existing computing resources available in the institution.