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The goal of the Apache Struts project is to encourage application architectures based on the "Model 2" approach, a variation of the classic Model-View-Controller (MVC) design paradigm. Under Model 2, a servlet (or equivalent) manages business logic execution, and presentation logic resides mainly in server pages.

NekoPull is an XML document parsing API that extends the Xerces Native Interface (XNI) to provide pull parsing functionality. The pull parsing paradigm is different from tree-based APIs like the Document Object Model (DOM) and event stream APIs like the Simple API for XML (SAX) in that the application controls the parsing of documents. NekoPull is similar to lex in that the application requests, or pulls, the document information one piece at a time instead of having document information pushed to the application.

JPA Security is an Access Control Solution for the Java Persistence API (JPA). With JPA Security your access control can be performed in the database. You may query the database for all objects of a certain type and will receive only the objects you have read access for. This filtering is performed in the database. Unaccessible queried objects will not be loaded into memory. JPA Security enables you to completely remove security-related code from your code-base. All access control may be configured via Annotations or XML. With JPA Security you do not have to change your access control paradigm (but maybe you want to, when you see the great capability of JPA Security). You even can mix access control paradigms easily. JPA Security is not designed to replace current security solutions, but to extend them. It integrates smoothly into the security mechanisms of the Java EE Platform, but may be used with third-party frameworks like Spring Security or in Java SE, too. With the extensibility of JPA Security it is easy to provide your own access control paradigm, access rules storage or login mechanism.

The core of the Struts framework is a flexible control layer based on standard technologies like Java Servlets, JavaBeans, ResourceBundles, and Extensible Markup Language (XML), as well as various Jakarta Commons packages. Struts encourages application architectures based on the Model 2 approach, a variation of the classic Model-View-Controller (MVC) design paradigm. Struts provides its own Controller component and integrates with other technologies to provide the Model and the View. For the Model, Struts can interact with any standard data access technology, including Enterprise Java Beans, JDBC, and Object Relational Bridge. For the View, Struts works well with JavaServer Pages, including JSTL and JSF, as well as Velocity Templates, XSLT, and other presentation systems. The Struts framework provides the invisible underpinnings every professional web application needs to survive. Struts helps you create an extensible development environment for your application, based on published standards and proven design patterns.

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.

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.