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Distributed data mining (DDM) is the semi-automatic pattern extraction of distributed data sources. The next generation of the data mining studies will be distributed data mining for many reasons. First of all, most of the current used data mining techniques require all data to be resident in memory, i.e., the mining process must be done at the data source site. This is not feasible for the exponential growth of the data stored in organization(s) databases. Another important reason is that data is inherently distributed for fault tolerance purposes. DDM requires two main decisions about the DDM implementations: A distributed computation paradigm (message passing, RPC, mobile agents), and the used integration techniques (Knowledge probing, CDM) in order to aggregate and integrate the results of the various distributed data miners. Recently, the new distributed computation paradigm, which has been evolved as mobile agent is widely used. Mobile agent is a thread of control that can trigger the transfer of arbitrary code to a remote computer. Mobile agents paradigm has several advantages: Conserving bandwidth and reducing latencies. Also, complex, efficient and robust behaviours can be realized with surprisingly little code. Mobile agents can be used to support weak clients, allow robust remote interaction, and provide scalability. In this paper, we propose a new model that can benefit from the mobile agent paradigm to build an efficient DDM model. Since the size of the data to be migrated in the DDM process is huge, our model will overcome the communication bottleneck by using mobile agents paradigm. Our model divides the DDM process into several stages that can be done in parallel on different data sources: Preparation stage, data mining stage and knowledge integration stage. We also include a special section on how current e-business models can use our model to reinforce the decision support in the organization. A cost analysis in terms of time consumed by each minor process (communication or processing) is given to illustrate the overheads of this model and

RETRO-DYNAMICS AND E-BUSINESS MODEL APPLICATION FOR DISTRIBUTED DATA MINING USING MOBILE AGENTS