Download JAR files tagged by relational with all dependencies

Persistence Annotation for RDF (PAR) is a set of annotations and an entity manager that provides JPA like functionality on top of an RDF store while accounting for and exploiting the fundamental differences between graph storage and relational storage. PAR introduces three (3) annotations that map a RDF triple (subject, predicate, object) to a Plain Old Java Object (POJO) using Java's dynamic proxy capabilities.

Village is a 100% Pure Java API that sits on top of the JDBC API. The purpose of this API is to make it easier to interact with a JDBC compliant relational database. By using Village, it is possible to execute and manage select/insert/update/delete SQL statements without having to actually write a single line of SQL. Village can use any valid JDBC Connection object, so it is possible to use Village with a JDBC Connection pool.

Apache Empire-db is an Open Source relational data persistence component which allows database vendor independent dynamic query definition as well as safe and simple data retrieval and updating. Compared to most other solutions like e.g. Hibernate, TopLink, iBATIS or JPA implementations, Empire-db takes a considerably different approach, with a special focus on compile-time safety, reduced redundancies and improved developer productivity.

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.

The Brown-UMBC Reinforcement Learning and Planning (BURLAP) Java code library is for the use and development of single or multi-agent planning and learning algorithms and domains to accompany them. The library uses a highly flexible state/observation representation where you define states with your own Java classes, enabling support for domains that discrete, continuous, relational, or anything else. Planning and learning algorithms range from classic forward search planning to value-function-based stochastic planning and learning algorithms.