MOA: A Java Framework for Data Stream Mining, and What Its GPL-3.0 Licence Costs You
MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.
At a glance
- What is it?
- MOA bundles classification, regression, clustering, outlier detection, drift detection and recommender algorithms with evaluation tooling for streams that never fit in memory. It is a benchmark suite first, a library second, and its GPL-3.0 licence is the constraint most teams discover last.
- Who is it for?
- Adopt MOA if you are doing stream mining research, teaching, or benchmarking where the GPL-3.0 licence is acceptable and you need a shared baseline that reviewers and collaborators recognise. Do not adopt it as an embedded component of a closed-source product without resolving the licence question first, since GPL-3.0 is copyleft and this article gives no legal advice on what that means for your distribution model.
- Can I use it commercially?
- Yes, with conditions. GPL-3.0 is a copyleft licence: if you distribute software that includes it, you must release that software's source code under the same licence. Running it internally without distributing it does not trigger that obligation.
- Is it still maintained?
- Yes. The repository last received commits 16 days ago.
- What is it written in?
- Mainly Java, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on September 15, 2026, and from our analysis. They are not legal advice.
DEEP OPEN-SOURCE ANALYSIS
The problem MOA addresses: models that cannot see the whole dataset twice
Most machine learning tooling assumes you can load a dataset, iterate over it, and iterate again. Stream mining breaks that assumption. Data arrives continuously, memory is bounded, and the underlying distribution may shift while the model is still learning. MOA is built for that setting. The README describes it as performing "BIG DATA stream mining in real time, and large scale machine learning", and the repository topics confirm the scope: clustering, data-stream-mining, streaming-algorithms. The intended audience is narrow and specific. It is a research framework, and the README states the goal plainly: "to provide a benchmark suite for the stream mining community." That framing matters more than any feature list. MOA exists so that a new drift detector or classifier can be compared against existing ones on the same generators and the same evaluation measures. If your problem is batch scoring of a static CSV, MOA is the wrong tool and you will spend more time fighting its evaluation model than using it.
What is actually in the box: six algorithm families and an evaluation layer
The README lists the algorithm categories: classification, regression, clustering, outlier detection, concept drift detection and recommender systems. Alongside those it ships "tools for evaluation". The extension points are stated explicitly: MOA "can be extended with new mining algorithms, and new stream generators or evaluation measures." That third extension point, evaluation measures, is the one that distinguishes MOA from a general purpose ML library. In a stream setting you are not only asking whether a classifier is accurate, you are asking how its accuracy evolves as the stream progresses and whether it recovers after a change point. MOA's architecture puts stream generators and evaluation measures on the same footing as the learners, which is why it functions as a benchmark harness rather than a model zoo. The relationship to WEKA is stated in the README: MOA is "Related to the WEKA project" and, like WEKA, written in Java. The stated difference is scale, with MOA "scaling to more demanding problems." If you have used WEKA's Experimenter, the mental model transfers, but the unit of work is a stream rather than a dataset.
Getting MOA running: source build, Maven coordinates, Docker image
The README points to three practical entry points rather than embedding commands. First, a Getting Started page at moa.cms.waikato.ac.nz/getting-started/. Second, a dedicated tutorial for building from source, titled "Building MOA from the source", at moa.cms.waikato.ac.nz/tutorial-6-building-moa-from-the-source/. Third, a Docker image published as waikato/moa on Docker Hub, which the README advertises with a badge. For Java projects, the Maven Central badge links to the nz.ac.waikato.cms group, and the artifact shown in the badge path is moa-pom, so the dependency coordinate family is nz.ac.waikato.cms.moa. Note the qualifier: the badge references moa-pom, which is a parent POM artifact, not necessarily the single jar you want to depend on. Before wiring it into a build, check the Maven Central listing for the group to see which artifacts correspond to which modules. The README does not enumerate module names or give a copy-paste dependency block, so treat the build file as something you read rather than something you copy from this page. The repository also shows a Travis CI build badge for the master branch, which tells you continuous integration exists but says nothing about the current state of the build.
The GPL-3.0 licence is the adoption decision, not an afterthought
MOA is licensed GPL-3.0, confirmed by both the repository metadata and the licence badge in the README. This is the single most consequential fact for commercial adopters, and it is easy to miss because the README's framing is academic. GPL-3.0 is a copyleft licence. If you link MOA into a distributed application, the obligations attach to that distribution. The README itself does not discuss this at all. It describes the project as a benchmark suite for a research community, and the citation request points to a 2010 JMLR paper by Bifet, Holmes, Kirkby and Pfahringer. Nothing in the supplied material tells you how the project intends commercial use to work, whether any alternative licensing is offered, or how the maintainers interpret the boundary between using MOA as a tool and linking it as a library. This article gives no legal advice. The practical point is narrower: if you are evaluating MOA for a product rather than a paper, the licence question belongs in the first meeting, not the last, and it needs an answer from someone qualified to give one. For research, teaching and internal benchmarking where you are not distributing a derivative work, the licence is usually a non-issue.
Release cadence and what maintenance actually looks like
The release history shows 2024.07.0 in July 2024, 2023.04.0 in April 2023, and 2021.07.0 in July 2021. That is roughly annual to biennial, with a gap of about two years between the 2021 and 2023 releases. The versioning scheme is date-based, so the number tells you when, not what changed. The supplied material includes no changelog, no release notes, and no deprecation policy, so you cannot tell from this page whether 2024.07.0 is a feature release, a dependency bump, or a compatibility fix. Plan accordingly: if you need a specific algorithm added in the last two years, verify it exists in the tagged release rather than assuming master and the release agree. The last push timestamp on the default branch is recent, which indicates ongoing commits between releases, but commits between releases are not the same as a supported version. The upgrade cost is dominated by the Java toolchain and by the fact that MOA sits in the WEKA ecosystem, so a major version bump in a shared dependency can ripple. Budget for reading the source when something breaks, because the README routes all support to two Google Groups mailing lists, moa-users and moa-development, rather than to an issue tracker with a triage process. Mailing lists are fine for research software. They are a poor fit if you need a response time commitment.
Where MOA stops being the right answer
The clearest failure mode is scope mismatch. MOA is a stream mining benchmark suite, not a general streaming data platform. It does not claim to handle ingestion, transport, storage, or deployment. If your actual problem is moving events from Kafka into a feature store, MOA contributes nothing to that layer and you should not evaluate it for it. A second limitation is the interaction model. The README's framing and the linked resources point to a desktop-oriented workflow: a Getting Started guide, a source build tutorial, a GUI-adjacent task structure inherited from the WEKA lineage. That is excellent for interactive experimentation and awkward for a headless production pipeline. You can use MOA programmatically through its Java API, but the supplied material does not document that path in any detail, so you are reading source. A third issue is language lock-in. MOA is Java, and the README makes no mention of Python bindings, a REST interface, or a service mode. If your team works in Python, you are either embedding a JVM or reimplementing the algorithm. Finally, the evaluation-first design cuts both ways: the abstractions that make benchmarking clean can feel like overhead when you only want to fit one model to one stream.
The real alternative: river, and the difference in approach
For online machine learning in Python, the closest comparison is river (formerly creme and scikit-multiflow, the latter of which was itself a stream learning project). The difference is not a feature checklist, it is the shape of the abstraction. River is built around a Python estimator interface: you call learn_one and predict_one on individual observations, and you compose pipelines from those. It targets incremental learning as a general capability, so the same object works for a single stream in a script and for a larger pipeline. MOA is built around evaluation as the primary activity. Its unit of work is a task: a stream generator, a learner, and an evaluation measure, run together and producing an outcome you compare against other learners. That is why MOA ships concept drift detectors and stream generators as first-class citizens rather than as utilities. If you want to publish a comparison against established baselines on standard generators, MOA's design saves you real work. If you want to add an online model to an existing Python service, river fits without a JVM in the loop. Neither is strictly better. Choosing MOA means choosing Java and GPL-3.0. Choosing river means giving up the benchmark-suite framing and the specific set of MOA algorithms and measures that reviewers in this field expect to see.
Editorial conclusion
Adopt MOA if you are doing stream mining research, teaching, or benchmarking where the GPL-3.0 licence is acceptable and you need a shared baseline that reviewers and collaborators recognise. Do not adopt it as an embedded component of a closed-source product without resolving the licence question first, since GPL-3.0 is copyleft and this article gives no legal advice on what that means for your distribution model. Before committing, verify three things against your own build: that the 2024.07.0 release compiles under your JDK, that the Docker image waikato/moa runs in your environment, and that the specific algorithm you need is actually present in the modules you plan to depend on rather than only in the GUI task list.
Community notes