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The Google Web Toolkit (GWT) Incubator fosters additional widgets and libraries for GWT before they are added to the core toolkit. The project is managed by the GWT engineering team at Google, and is used as a place to share, discuss, and vet future or speculative GWT features. Future releases of GWT may or may not use these features, but you are welcome to pull them from here to use today.

Liferay Document Library File Version Discussion Web

WebIDE Plugins

The Google Web Toolkit (GWT) Incubator fosters additional widgets and libraries for GWT before they are added to the core toolkit. The project is managed by the GWT engineering team at Google, and is used as a place to share, discuss, and vet future or speculative GWT features. Future releases of GWT may or may not use these features, but you are welcome to pull them from here to use today.

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This project implements converters for v1 protobuf objects into v2 protobuf objects, and vice versa. This package is intended for use for Dataflow, and should probably not be used by other users without a discussion on https://groups.google.com/forum/#!forum/google-cloud-bigtable-discuss first.

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, classification 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 existing 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.

RongCloud Android SDK

Library providing domain, daos and services for embedding commenting or discussions to an application. No UI elements included.

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.

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.

Repository tier customizations that add ability to @mention someone in a discussion or comment.

Implementation of discussment's DAO layer in Hibernate 3.x.

Project for embedding commenting or discussions into existing applications.

RongCloud Android SDK

Repository tier customizations that add ability to @mention someone in a discussion or comment.

Implementation of discussment's DAO layer in Hibernate 5.x.

Helper pom for build and deploy of JDK7 variants of the artifacts.

Implementation of discussment's DAO layer in JPA.

Library providing Wicket components for embedding commenting or discussions into existing applications.