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Chapitre D'ouvrage Année : 2018

Streaming Data Mining with Massive Online Analytics (MOA)

Résumé

Fast Big Data is being produced at high-velocity in real-time. To effectively deal with this type of streaming data produced in real time, we need to be able to adapt to changes on the distribution of the data being produced, and we need to do it using the minimum amount of time and memory. The Internet of Things (IoT) is a good example and motivation of this type of streaming data produced in real time. Massive Online Analytics (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 problem of scaling up the implementation of state of the art algorithms to real world dataset sizes. MOA includes classification and clustering methods. It contains collection of offline and online methods as well as tools for evaluation. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and is released under the GNU GPL license.
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Dates et versions

hal-04492586 , version 1 (06-03-2024)

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Citer

Albert Bifet, Jesse Read, Geoff Holmes, Bernhard Pfahringer. Streaming Data Mining with Massive Online Analytics (MOA). Data Mining in Time Series and Streaming Databases, 83, WORLD SCIENTIFIC, pp.1-25, 2018, Series in Machine Perception and Artificial Intelligence, ⟨10.1142/9789813228047_0001⟩. ⟨hal-04492586⟩
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