MOA is the most popular open source framework for data stream mining, with a very active growing community (blog). It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation. Related to the WEKA project, MOA is also written in Java, while scaling to more demanding problems.
MOA performs BIG DATA stream mining in real time, and large scale machine learning. MOA can be extended with new mining algorithms, and new stream generators or evaluation measures. The goal is to provide a benchmark suite for the stream mining community.
- MOA users: http://groups.google.com/group/moa-users
- MOA developers: http://groups.google.com/group/moa-development
If you want to refer to MOA in a publication, please cite the following JMLR paper:
Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer (2010); MOA: Massive Online Analysis; Journal of Machine Learning Research 11: 1601-1604
You can now upload the build artifact from the GitHub Actions workflow. Make sure to unzip it!
These steps assume you have Java installed and maven installed. If you don't have maven installed, you can download it from here. You can achieve the same outcome with IntelliJ IDEA by building moa with the IDE (The linked doc is a little out of date) and packaging it as a single jar file.
You can compile moa as a single jar file with all dependencies included by running the following command in the moa directory:
cd ./moa
mvn compile assembly:singleIf successful, the jar file will be built to a file like this moa/target/moa-2023.04.1-SNAPSHOT-jar-with-dependencies.jar with a different date.
One way to verify that the jar file was built correctly is to run the following command:
java -jar ./moa/target/moa-2023.04.1-SNAPSHOT-jar-with-dependencies.jarThis should start the MOA GUI.