Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Data Mining in Time Series and Streaming Databases |
| Publisher | World Scientific Publishing Co. Pte Ltd |
| Pages | 1-25 |
| Number of pages | 25 |
| ISBN (Electronic) | 9789813228047 |
| ISBN (Print) | 9789813228030 |
| Publication status | Published - 11 Jan 2018 |
| Externally published | Yes |