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Apache Synapse is designed to be a simple, lightweight and high performance Enterprise Service Bus (ESB) from Apache. Based on a small asynchronous core, Apache Synapse has excellent support for XML and Web services - as well as binary and text formats. The Synapse engine is configured with a simple XML format and comes with a set of ready-to-use transports and mediators. We recommend you start by reading the QuickStart and then trying out the samples.

Mahout's goal is to build scalable, Apache licensed machine learning libraries. Initially, we are interested in building out the ten machine learning libraries detailed in http://www.cs.stanford.edu/people/ang//papers/nips06-mapreducemulticore.pdf using Hadoop. While these algorithms are our initial focus, we welcome contributions of other machine learning approaches.

Apache ODE (Orchestration Director Engine) executes business processes written following the WS-BPEL standard. It talks to web services, sending and receiving messages, handling data manipulation and error recovery as described by your process definition. It supports both long and short living process executions to orchestrate all the services that are part of your application.

Apache ActiveMQ is the most popular and powerful open source Message Broker and Enterprise Integration Patterns provider.

Apache Tuscany simplifies the task of developing SOA solutions by providing a comprehensive infrastructure for SOA development and management that is based on Service Component Architecture (SCA) standard.

Apache CouchDB is a distributed, fault-tolerant and schema-free document-oriented database accessible via a RESTful HTTP/JSON API. Among other features, it provides robust, incremental replication with bi-directional conflict detection and resolution, and is queryable and indexable using a table-oriented view engine with JavaScript acting as the default view definition language.

Unstructured Information Management applications are software systems that analyze large volumes of unstructured information in order to discover knowledge that is relevant to an end user. UIMA is a framework and SDK for developing such applications. An example UIM application might ingest plain text and identify entities, such as persons, places, organizations; or relations, such as works-for or located-at. UIMA enables such an application to be decomposed into components, for example "language identification" -> "language specific segmentation" -> "sentence boundary detection" -> "entity detection (person/place names etc.)". Each component must implement interfaces defined by the framework and must provide self-describing metadata via XML descriptor files. The framework manages these components and the data flow between them. Components are written in Java or C++; the data that flows between components is designed for efficient mapping between these languages. UIMA additionally provides capabilities to wrap components as network services, and can scale to very large volumes by replicating processing pipelines over a cluster of networked nodes.

Felix is a community effort to implement the OSGi R4 Service Platform, which includes the OSGi framework and standard services, as well as providing and supporting other interesting OSGi-related technologies. The ultimate goal is to provide a completely compliant implementation of the OSGi framework and standard services and to support a community around this technology. Felix currently implements a large portion of the OSGi release 4 specification, but additional work is necessary for full compliance. Despite this fact, the OSGi framework functionality provided by Felix is very stable.

IBM® LanguageWare® is a set of run-time libraries and an easy-to-use Eclipse-based development environment for building custom text analyzers in various languages. Deployable in Apache UIMA, these analyzers can expose the information buried in text to any application. The Eclipse-based tools makes creating analyzers simple and fast, even for non-technical users. The tools make it easy to build dictionaries, ontologies, and rules for identifying key information, relationships and meaning.

In my previous article, I introduced the Map/Reduce model as a powerful model for parallelism. However, although Map/Reduce is simple, powerful, and provides a good opportunity to parallelize algorithm, it is based on a rigid procedural structure that requires injection of custom user code and therefore it is not easy to understand the big picture from a high level. You need to drill into the implementation code of the map and reduce functions in order to figure out what is going on.

ZooKeeper is a high-performance coordination service for distributed applications. It exposes common services - such as naming, configuration management, synchronization, and group services - in a simple interface so you don't have to write them from scratch. You can use it off-the-shelf to implement consensus, group management, leader election, and presence protocols. And you can build on it for your own, specific needs.

By integrating heterogeneous original data with uniform RDF format, creating additional ontology mappings between GO terms, and introducing a set of reasoning rulebases, we proposed a RDF model GORouter.

Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets.

Open Office Toolkig

Taste is a flexible, fast collaborative filtering engine for Java. The engine takes users' preferences for items ("tastes") and returns estimated preferences for other items. For example, a site that sells books or CDs could easily use Taste to figure out, from past purchase data, which CDs a customer might be interested in listening to.