Experimental Elearning Application for Distributed Data Mining Systems

The development of machine learning algorithms, computing and communication in recent years is producing a world that depends on information. Nowadays, most of the information is stored as raw data in real distributed database management systems. Although many scientific discoveries were made in research fields such as Distributed Databases, Data Preparation, Machine Learning, Distributed Data Mining and Elearning, there is a lack of experimental applications that facilitates the deepen of knowledge in all these research fields blended together. This paper presents an experimental Elearning application that allows students to assimilate knowledge through experiments from the aforementioned research domains. The application has a module that imports, prepares and transforms data in order to be processed by the data mining task The data is stored in the MySql database management system in a distributed manner, achieved through the replication process in a master-slave topology. Students can set the replication type: Statement, Row or Mixed Based Replication. The Data Mining task (classification) is achieved in a distributed manner using Distributed Committee Machines with a modified version of a multilayer perceptron proposed in our previous research (autoresetting multilayer perceptron). The users can choose from three standard data sets: iris', wine' and concl . Through the web interface, the users from the master system will send the configuration parameters for the neural network and the addresses of the distributed slave systems. In the application the students can visualize the classification results derived from the distributed experiments and choose the highest-scoring classifier.