MOA: a real-time analytics open source framework

Authors:
Albert Bifet;Geoff Holmes;Bernhard Pfahringer;Jesse Read;Philipp Kranen;Hardy Kremer;Timm Jansen;Thomas Seidl
Affiliations:
Department of Computer Science, University of Waikato, Hamilton, New Zealand;Department of Computer Science, University of Waikato, Hamilton, New Zealand;Department of Computer Science, University of Waikato, Hamilton, New Zealand;Department of Computer Science, University of Waikato, Hamilton, New Zealand;Data Management and Exploration Group, RWTH Aachen University, Germany;Data Management and Exploration Group, RWTH Aachen University, Germany;Data Management and Exploration Group, RWTH Aachen University, Germany;Data Management and Exploration Group, RWTH Aachen University, Germany
Venue:
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Year:
2011

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Abstract

Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA is designed to deal with the challenging problems of scaling up the implementation of state of the art algorithms to real world dataset sizes and of making algorithms comparable in benchmark streaming settings. It contains a collection of offline and online algorithms for classification, clustering and graph mining as well as tools for evaluation. For researchers the framework yields insights into advantages and disadvantages of different approaches and allows for the creation of benchmark streaming data sets through stored, shared and repeatable settings for the data feeds. Practitioners can use the framework to easily compare algorithms and apply them to real world data sets and settings. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis. Besides providing algorithms and measures for evaluation and comparison, MOA is easily extensible with new contributions and allows for the creation of benchmark scenarios.