Java Libraries

org.mortbay.jetty : jetty-integration-tests

This module is for testing all classes that require external dependencies not suitable for being included in migration to Eclipse

Last Version: 8.0.0.M3

Release Date: May 31, 2011

Hudson Maven

org.jvnet.hudson.main : hudson-maven

Hudson Maven Integration

Last Version: 2.0.1

Release Date: May 30, 2011

beanunit

nl.ivonet : beanunit

beanunit is a unit test (junit) extension for automated testing of beans.

Last Version: 2.0

Release Date: May 30, 2011

sbteclipse_2.9.0-1

com.weiglewilczek.sbteclipse : sbteclipse_2.9.0-1

Last Version: 0.7

Release Date: May 29, 2011

Mahout Utilities

org.apache.mahout : mahout-utils

Utilities for preparing content into formats for Mahout.

Last Version: 0.5

Release Date: May 28, 2011

Mahout Taste Webapp

org.apache.mahout : mahout-taste-webapp

Mahout Taste Collaborative Filtering Web App

Last Version: 0.5

Release Date: May 28, 2011

org.apache.mahout : mahout-eclipse-support

Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classfication and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms. Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come to the mailing lists to find out more. Currently Mahout supports mainly four use cases: Recommendation mining takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents and groups them into groups of topically related documents. Classification learns from exisiting categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session, shopping cart content) and identifies, which individual items usually appear together.

Last Version: 0.5

Release Date: May 28, 2011

Interfaces

org.imixs.workflow : imixs-workflow-xml

Imixs Workflow XML provides xml libraries to extend the Imixs Workflow API based on xml standards.

Last Version: 2.1.1

Release Date: May 28, 2011

Workflow API

org.imixs.workflow : imixs-workflow-api

Imixs Workflow API provides the platform for an open and standardized workflow technology. Imixs Workflow API provides various components in a Java framework that allow to create, control and monitor process-oriented business tasks. The following documentation describes the most important concepts and fundamentals of the Imixs Workflow API. The documentation does not cover each detail of the implementation.

Last Version: 2.1.2

Release Date: May 28, 2011