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Doesn't support anymore.

A fork of JBoss JNPServer because it is not maintained anymore

A fork of JBoss JNPServer because it is not maintained anymore

This extension uses request parameters to populate path variables. Used parameters are removed so they won't be sent in the query string or body anymore.

Not required anymore and thus will not be supported. Parent pom providing dependency and plugin management for Livedata SDK. Not required when using fatjar.

Elasticsearch pipeline is a project for synchronizing data from database, also convert data first before we push data to elasticsearch, the current solutions from logstash does not help anymore, so we make our own.

GWT pages to be included in the "Design" plugin packaged with SonarQube. Extracting this project outside SQ sources allows to reduce significantly compilation time. GWT is no more supported in SQ and this project does not evolve anymore. It will be replaced sooner or later.

Scriptler allows you to store/edit/execute groovy scripts on any of the slaves/nodes... no need to copy paste groovy code anymore. Beside administer your scripts, Scritpler also provides a way to share scripts between users via hosted script catalogs on the internet.

@Sumatera Model converter from ORM class to regular pojo class, the ORM term is a modelling class that doesn't like regular pojo, such like @Entity from JPA/Hibernate and so on. It can be used for mapping regular object, post data in request body where you cannot put ORM class directly in a parameter, or you cannot convert to json directly, or any other problem regarding it. You don't need anymore to create duplicate Dto / Pojo class which represent ORM class, just write your ORM class and annotate it with @Sumatera, then the regular pojo class would be generated for you.

This project contains a Java implementation of median relational generalized learning vector quantization as proposed by Nebel, Hammer, Frohberg, and Villmann (2015, doi:10.1016/j.neucom.2014.12.096). Given a matrix of pairwise distances D and a vector of labels Y it identifies prototypical data points (i.e. rows of D) which help to classify the data set using a simple nearest neighbor rule. In particular, the algorithm optimizes the generalized learning vector quantization cost function (Sato and Yamada, 1995) via an expectation maximization scheme where in each iteration one prototype 'jumps' to another data point in order to improve the cost function. If the cost function can not be improved anymore for any of the data points, the algorithm terminates.