MOA (Massive On-line Analysis) is a framework for data stream mining. It includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, it is also written in Java, while scaling to more demanding problems. The goal of MOA is a benchmark framework for running experiments in the data stream mining context by proving
- storable settings for data streams (real and synthetic) for repeatable experiments
- a set of existing algorithms and measures form the literature for comparison and
- an easily extendable framework for new streams, algorithms and evaluation methods.
The workflow in MOA follows the simple schema depicted below: first a data stream (feed, generator) is chosen and configured, second an algorithm (e.g. a classifier) is chosen and its paramters are set, third the evaluation method or measure is chosen and finally the results are obtained after running the task.
To run an experiment using MOA, the user can choose between a graphical user interfacxe (GUI) or a command line execution. Users should probably start by watching the demo video (see downloads) or download the software and try on an example. Developers can easily extend all three parts of the above architecture to include and test new methods (see details).