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https://github.com/apache/incubator-samoa

Mirror of Apache Samoa (Incubating)
https://github.com/apache/incubator-samoa

samoa

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Mirror of Apache Samoa (Incubating)

Host: GitHub
URL: https://github.com/apache/incubator-samoa
Owner: apache
License: apache-2.0
Created: 2015-01-27T08:00:05.000Z (over 9 years ago)
Default Branch: master
Last Pushed: 2023-04-16T01:52:11.000Z (about 1 year ago)
Last Synced: 2024-02-07T15:07:33.919Z (5 months ago)
Topics: samoa
Language: Java
Size: 5.77 MB
Stars: 243
Watchers: 41
Forks: 112
Open Issues: 16
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE

Lists

awesome-streaming - Apache Samoa - distributed streaming machine learning (ML) framework that contains a programing abstraction for distributed streaming ML algorithms. (Table of Contents / Online Machine Learning)
awesome-streaming - Apache Samoa - distributed streaming machine learning (ML) framework that contains a programing abstraction for distributed streaming ML algorithms. (Table of Contents / Online Machine Learning)

README

[![Build Status](https://travis-ci.org/apache/incubator-samoa.svg?branch=master)](https://travis-ci.org/apache/incubator-samoa)

Apache SAMOA: Scalable Advanced Massive Online Analysis.
=================
Apache SAMOA is a platform for mining on big data streams.
It is a distributed streaming machine learning (ML) framework that contains a
programing abstraction for distributed streaming ML algorithms.

Apache SAMOA enables development of new ML algorithms without dealing with
the complexity of underlying streaming processing engines (SPE, such
as Apache Storm and Apache S4). Apache SAMOA also provides extensibility in integrating
new SPEs into the framework. These features allow Apache SAMOA users to develop
distributed streaming ML algorithms once and to execute the algorithms
in multiple SPEs, i.e., code the algorithms once and execute them in multiple SPEs.

## Build

### Storm mode

Simply clone the repository and install SAMOA.

```bash
git clone http://git.apache.org/incubator-samoa.git
cd incubator-samoa
mvn -Pstorm package
```

The deployable jar for SAMOA will be in `target/SAMOA-Storm-0.3.0-SNAPSHOT.jar`.

### S4 mode

If you want to compile SAMOA for S4, you will need to install the S4 dependencies
manually as explained in [Executing SAMOA with Apache S4](http://samoa.incubator.apache.org/documentation/Executing-SAMOA-with-Apache-S4.html).

Once the dependencies if needed are installed, you can simply clone the repository and install SAMOA.

```bash
git clone http://git.apache.org/incubator-samoa.git
cd incubator-samoa
mvn -Ps4 package
```

### Apex mode

Simply clone the repository and and create SAMOA with Apex package.
```bash
git clone http://git.apache.org/incubator-samoa.git
cd incubator-samoa
mvn -Papex package
```

### Local mode

If you want to test SAMOA in a local environment, simply clone the repository and install SAMOA.

```bash
git clone http://git.apache.org/incubator-samoa.git
cd incubator-samoa
mvn package
```

The deployable jar for SAMOA will be in `target/SAMOA-Local-0.3.0-SNAPSHOT.jar`.

## Documentation

The documentation is intended to give an introduction on how to use Apache SAMOA in the various possible ways.
As a user you can use it to develop new algorithms and test different Distributed Stream Processing Engines.

[Documentation](http://samoa.incubator.apache.org/documentation/Home.html)

[Javadoc](http://samoa.incubator.apache.org/docs/api/)

## Slides

[![SAMOA Slides](http://samoa.incubator.apache.org/samoa-slides.jpg)](https://speakerdeck.com/gdfm/samoa-a-platform-for-mining-big-data-streams)

G. De Francisci Morales, A. Bifet [SAMOA: Scalable Advanced Massive Online Analysis](http://jmlr.csail.mit.edu/papers/volume16/morales15a/morales15a.pdf)
Journal of Machine Learning Research, 16(Jan):149−153, 2015.

## Apache SAMOA Developer's Guide

## Contributors
[List of contributors to the Apache SAMOA project](http://samoa.incubator.apache.org/documentation/Team.html)

## License

The use and distribution terms for this software are covered by the
Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0.html).