Download JAR files tagged by itself with all dependencies

J2EE integrated development framework,for the rapid build enterprise applications.Based on Struts,Spring,Hibernate constructed to provide Web systems development functions commonly used components. Simply configure can be solved the general problems in the process of developing encountered. For example,already has built in login interface,user and role management,base AOP paging,logging feature and a lot more. It's a Java package, a class library for Java programmers. It's not an application for end-users in itself, but something that programmers can embed into their products.

--------------------------------------------------------------------------- Version for VIVO 1.8 This uses the source and test code from the Version for VIVO 1.10, edited to make it compatible with VIVO 1.8. It also borrows some code from VIVO 1.10 itself, changes the package name to avoid conflicts, and adds it here. * Vitro 1.8 uses Jena 2, not Jena 3, and Commons Lang 2, not 3, so modify the "import" statements in the Data Distribution code accordingly. * Vitro 1.8 does not have a "...webapp.utils.sparqlrunner" package, so borrow the source from Vitro 1.10, move it to a different package (just for compatibility with the 1.9 version), modify it to use Jena 2 and Commons Lang 2, and modify the Data Distribution API code to use this sparqlrunner package. * Vitro 1.9 has an earlier version of "...webapp.utils.configuration" package, so borrow the source from Vitro 1.10, move it to a different package so it won't conflict with the 1.9 version, modify it to use Jena 2 and Commons Lang 2, and modify the Data Distribution API code to use this configuration package. Make these modifications, compile, JAR it up, and deploy. --------------------------------------------------------------------------- Because VIVO 1.8 is not available as a Maven package, we have extracted the class files, JARed them up, and stored them in a file-based repository within this project. But that doesn't include any transitive dependencies, so any package that this code requires must be explicitly listed as a dependency. ---------------------------------------------------------------------------

This is a specialized version of the Clirr Maven Plugin. It adds capabilities for excluding specific error types, as well as separating code into three, rather than two, subgroups: Internal code (no checks) Externally invoked code (Annotated with an "externally invoked" annotation, same as Externally implemented, but adding methods to interfaces and abstract classes is allowed) Externally implemented code (Assumed default. Full backwards compatibility required *unless* an interface is annotated with a defined adaptor annotation, in which case full backwards compatibility is required for the adaptor class, but the rules of @ExternallyInvoked apply to the interface itself) Clirr is a tool that checks Java libraries for binary and source compatibility with older releases. Basically you give it two sets of jar files and Clirr dumps out a list of changes in the public API. The clirr-maven-plugin can be configured to break the build, if it detects incompatible api changes. In a continuous integration process, the clirr-maven-plugin can automatically prevent accidental introduction of binary or source compatibility problems. Additionally, the plugin can generate a report as part of the generated site.

This is the brand new, re-written from scratch plugin for ANTLR v3. Previous valiant efforts all suffered from being unable to modify the ANTLR Tool itself to provide support not just for Maven oriented things but any other tool that might wish to invoke ANTLR without resorting to the command line interface. Rather than try to shoe-horn new code into the existing Mojo (in fact I think that by incorporating a patch supplied by someone I ended up with tow versions of the Mojo, I elected to rewrite everything from scratch, including the documentation, so that we might end up with a perfect Mojo that can do everything that ANTLR v3 supports such as imported grammar processing, proper support for library directories and locating token files from generated sources, and so on. In the end I decided to also change the the ANTLR Tool.java code so that it would be the provider of all the things that a build tool needs, rather than delegating things to 5 different tools. So, things like dependencies, dependency sorting, option tracking, generating sources and so on are all folded back in to ANTLR's Tool.java code, where they belong, and they now provide a public interface to anyone that might want to interface with them. One other goal of this rewrite was to completely document the whole thing to death. Hence even this pom has more comments than funcitonal elements, in case I get run over by a bus or fall off a cliff while skiing. Jim Idle - March 2009

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.