Download JAR files tagged by performed with all dependencies

This plugin generates a MindMap from the project dependencies. The mindmap file (with the '.mm' extension) can be viewed with Freeplane (free tool). By default, the mindmap shows the full dependency tree. You can filter this tree at generation time by artifact's group id, by providing a parameter 'groupIdsFilteringREGEXMatch' setted with a groupId name fragment (a startswith will be performed). Usage is (for exemple) : mvn fr.xebia.maven.plugins:mindmap-maven-plugin:1.0.0-SNAPSHOT:mindmap with filtering : mvn fr.xebia.maven.plugins:mindmap-maven-plugin:1.0.0-SNAPSHOT:mindmap -DgroupIdsFilteringREGEXMatch=fr.xebia

This generates a schema artifact which contains all of the schemas from xmpp.org's subversion repository. Generating this artifact enables a schema to be referenced by jaxb using the ClassPathCatalogResolver and a catalog file. To refresh the content of this artifact, either remove the src/main/resources directory manually or run a clean build from hudson. This is because maven-scm-plugin is set to checkout only if that directory does not exist so that the remote subversion repository is not hit every time a build is performed.

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 Virtuoso server module (ks-server-virtuoso) provides an implementation of the Triple Store server-side component (API in ks-triplestore) on top of the OpenLink Virtuoso triple store, a scalable native code triple store that is accessed as an external service. The Virtuoso Sesame driver, which builds on the Virtuoso JDBC driver, is used to remotely access the Virtuoso server for writing data and performing SPARQL queries. For performance reasons, data modification is performed in a non-transactional way, with the module managing possible failures in a way that trigger the external repopulation of the Virtuoso triple store starting from the master copy of data stored in the Data Store component.

Advanced Library to Remove Object Relational Impedance. DBvolution translates all database concepts into Object Oriented concepts, allowing you to spend all your time writing Java rather than fixing broken and awkward SQL. DBvolution transforms your schema into classes, reduces the database configuration to sparse annotations on the classes, and allows querying directly from the classes. Queries are created inside your java code and takes as little as one line for a multi-table outer join. Retrieving the rows from the query is only one more method call. Dozens of SQL functions are available without leaving your Java code and use chaining to build complex expressions easily. Transactions are encapsulated into a thread-like API, allowing you to write complex database interactions in complete safety. The queries performed by DBvolution are always available for debugging and checking by DBAs before release. There are examples in nz.co.gregs.dbvolution.examples, and documentation at http://dbvolution.gregs.co.nz.

Advanced Library to Remove Object Relational Impedance. DBvolution translates all database concepts into Object Oriented concepts, allowing you to spend all your time writing Java rather than fixing broken and awkward SQL. DBvolution transforms your schema into classes, reduces the database configuration to sparse annotations on the classes, and allows querying directly from the classes. Queries are created inside your java code and takes as little as one line for a multi-table outer join. Retrieving the rows from the query is only one more method call. Dozens of SQL functions are available without leaving your Java code and use chaining to build complex expressions easily. Transactions are encapsulated into a thread-like API, allowing you to write complex database interactions in complete safety. The queries performed by DBvolution are always available for debugging and checking by DBAs before release. There are examples in nz.co.gregs.dbvolution.examples, and documentation at http://dbvolution.gregs.co.nz.

Advanced Library to Remove Object Relational Impedance. DBvolution translates all database concepts into Object Oriented concepts, allowing you to spend all your time writing Java rather than fixing broken and awkward SQL. DBvolution transforms your schema into classes, reduces the database configuration to sparse annotations on the classes, and allows querying directly from the classes. Queries are created inside your java code and takes as little as one line for a multi-table outer join. Retrieving the rows from the query is only one more method call. Dozens of SQL functions are available without leaving your Java code and use chaining to build complex expressions easily. Transactions are encapsulated into a thread-like API, allowing you to write complex database interactions in complete safety. The actions performed by DBvolution are always available for debugging and checking by DBAs before release. There are examples in nz.co.gregs.dbvolution.examples, and documentation at http://dbvolution.gregs.co.nz and http://sourceforge.net/p/dbvolution/blog/

Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting. The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy). The best point in the grid is then taken and a 10-fold CV is performed with the adjacent parameter pairs. If a better pair is found, then this will act as new center and another 10-fold CV will be performed (kind of hill-climbing). This process is repeated until no better pair is found or the best pair is on the border of the grid. In case the best pair is on the border, one can let GridSearch automatically extend the grid and continue the search. Check out the properties 'gridIsExtendable' (option '-extend-grid') and 'maxGridExtensions' (option '-max-grid-extensions <num>'). GridSearch can handle doubles, integers (values are just cast to int) and booleans (0 is false, otherwise true). float, char and long are supported as well. The best filter/classifier setup can be accessed after the buildClassifier call via the getBestFilter/getBestClassifier methods. Note on the implementation: after the data has been passed through the filter, a default NumericCleaner filter is applied to the data in order to avoid numbers that are getting too small and might produce NaNs in other schemes.