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Interactively classify through visual means. You are Presented with a scatter graph of the data against two user selectable attributes, as well as a view of the decision tree. You can create binary splits by creating polygons around data plotted on the scatter graph, as well as by allowing another classifier to take over at points in the decision tree should you see fit. For more information see: Malcolm Ware, Eibe Frank, Geoffrey Holmes, Mark Hall, Ian H. Witten (2001). Interactive machine learning: letting users build classifiers. Int. J. Hum.-Comput. Stud. 55(3):281-292.

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.

R can use RJava or jsr223 to communicate with java. R also has a class system called R6. If you want to use a java library with native rJava or jsr223 in R there is potentially a lot of glue code needed, and R library specific packaging configuration required. However if you don't mind writing an R-centric API in Java you can generate all of this glue code using a few java annotations and the normal javadoc annotations. This plugin aims to provide an annotation processor that writes that glue code and creates a fairly transparent connection between Java code and R code, with a minimum of hard work. The focus of this is streamlining the creation of R libraries by Java developers, rather than allowing access to arbitrary Java code from R. The ultimate aim of this plugin to allow java developers to provide simple APIs for their libraries, package their library using Maven, push it to github and for that to become seamlessly available as an R library, with a minimal amount of fuss. A focus is on trying to produce CI ready libraries tested with Github workflows and ready for CRAN submission.

relay-jdbc allows a JDBC connection to be relayed through an intermediate relay server: it is both a JDBC driver (client) and a JDBC relay server. It is a fork of https://github.com/AugeoSoftware/VJDBC project, where Aleksei Yu. Semenov has implemented a number of brilliant improvements of the orignal VJDBC project hosted on http://vjdbc.sourceforge.net Original Developers were: Hunter Payne ([email protected]) and Michael Link ([email protected]). For all pre-existing code, credits go to the original respective author. This product brings the original VJDBC concept further and (apart from code quality improvements and bug fixes) implements complete separation of the underlying transport mechanism, introducing support for connecting directly through a SSH pipe, where the relay server interacts with the client through standard I/O pipes. The re-designed transport architecture supports easy development of new transport implementations, allowing this JDBC relay to be quickly adapted to a variety of exotic environments.

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.

Modelant provides JMI-compliant mechanism to read, process, export models in MOF 1.4, UML 1.3 and UML 1.4, including also an engine for generation of code (any texts) using Object-Oriented Programming and templates. It is an extensible framework allowing adding support of other languages, described using MOF 1.4 metamodels. Modelant provides Maven plugins to automate the creation and comparison of UML 1.3, UML 1.4, MOF 1.4 models; the generation of code from models; the reverse engineering of java code, relational databases and XML DTD/XSD; the transformatoion of models, e.g. tranformation of of UMl 1.3 models to UML 1.4; the comparison and change identification in UML 1.3/UML 1.4 models and MOF 1.4 metamodels. The produced UML 1.3 models are suitable to be imported in Rational Rose 2003 + XMI Plugin, WhiteStar UML / Star UML, Enterprise Architect, other, as they employ MOF 1.3/MOF 1.4 as reading MOF 1.3 is internally converted to MOF1.4 and XMI 1.1 and XMI 1.2. whereas the converted to UML 1.4 models can be imported in Argo UML, Enterprise Architect, other as they employ MOF 1.4 and XMI 1.1 and XMI 1.2. Modelant wraps the NetBeans Meta-Data Repository (MDR).

Luzzu is a Quality Assessment Framework for Linked Open Datasets. It is a generic framework based on the Dataset Quality Ontology (daQ), allowing users to define their own quality metrics. Luzzu is an integrated platform that: - assesses Linked Data quality using a library of generic and user-provided domain specific quality metrics in a scalable manner; - provides queryable quality metadata on the assessed datasets; - assembles detailed quality reports on assessed datasets. Furthermore, the infrastructure: - scales for the assessment of big datasets; - can be easily extended by the users by creating their custom and domain-specific pluggable metrics, either by employing a novel declarative quality metric specification language or conventional imperative plugins; - employs a comprehensive ontology framework for representing and exchanging all quality related information in the assessment workflow; - implements quality-driven dataset ranking algorithms facilitating use-case driven discovery and retrieval.