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JaCaDDM is an agents & artifacts oriented Distributed Data Mining (DDM) tool that entails the creation and testing of Learning Strategies. A Learning Strategy is an encapsulated DDM workflow modeling the interaction of agents with their environment (which includes DM tools) and other agents, with the objective to create a classification model from distributed data.

Learning strategies are meant to be general in the sense that they can be applied to any distributed setting, deployment details are managed by the JaCaDDM platform. In this sense, learning strategies are plug-and-play. The JaCaDDM distribution already comes with a set of different learning strategies to try, and it is also possible to add new ones.

JaCaDDM considers as a distributed setting any kind of environment where data is split in various sites (even geographically distributed). With JaCaDDM is possible to configure and launch a deployment that takes into account the different sites, and their data, that participate in the DDM process. As mentioned, the actual process is encapsulated on the learning strategy, which may have some configurable parameters that can be set as part of the general configuration.

JaCaDDM provides a tool to experiment and do research with different DDM approaches, as it makes an evaluation of the produced classification model, yielding various performance statistics (total time, classification accuracy, network traffic produced, model complexity, confusion matrix).

JaCaDDM can be extended through the adding of new learning strategies and artifacts. Artifacts are first-class entities in the agent environment that encapsulate services, in the case of JaCaDDM, these services consists on DM related tools.

More details can be found in the following wiki pages:

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