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MASON is a fast discrete-event multiagent simulation library core in Java, designed to be the foundation for large custom-purpose Java simulations, and also to provide more than enough functionality for many lightweight simulation needs. MASON contains both a model library and an optional suite of visualization tools in 2D and 3D.

The Java Advanced Imaging API extends the Java 2 platform by allowing sophisticated, high-performance image processing to be incorporated into Java applets and applications. It is a set of classes providing imaging functionality beyond that of Java 2D and the Java Foundation classes, though it is designed for compatibility with those APIs. This API implements a set of core image processing capabilities including image tiling, regions of interest, deferred execution and a set of core image processing operators, including many common point, area, and frequency domain operators.

DataVision is an Open Source reporting tool similar to Crystal Reports. Reports can be designed using a drag-and-drop GUI. They may be run, viewed, and printed from the application or exported as HTML, XML, PDF, Excel, LaTeX2e, DocBook, or tab- or comma-delimited text files. The output files produced by LaTeX2e and DocBook can in turn be used to produce PDF, text, HTML, PostScript, and more.

The Java Data Objects 2.0 (JDO) API is a standard interface-based Java model abstraction of persistence, developed as Java Specification Request JSR 243 under the auspices of the Java Community Process. The JDO TCK is a test suite designed to test implementations of the Java Data Objects specification. In order to claim compatibility with JDO, an implementation must pass all relevant tests of the JDOTCK. NOTE!! There are no unit tests in this project.

D-Haven Managed Pool is a library designed to provide pools that are asynchronously managed in a background thread. The pool system is very flexible and can accomodate just about every need. It boasts the ability to add pool listeners so that you can instrument and intercept the pooled objects when they are created, destroyed, acquired, and released.

D-Haven Event is a library designed to make it easier to develop event based processing systems. It also includes a CommandManager to handle certain activities behind the scenes in a controlled number of background threads. The library has been fully tested, and all dependencies have been brought to a minimum.

D-Haven Event is a library designed to make it easier to develop event based processing systems. It also includes a CommandManager to handle certain activities behind the scenes in a controlled number of background threads. The library has been fully tested, and all dependencies have been brought to a minimum.

InteGraal has been designed in a modular way, in order to facilitate software reuse and extension. It should make it easy to test new scenarios and techniques, in particular by combining algorithms. The main features of Graal are currently the following: (1) internal storage to store data by using a SQL or RDF representation (Postgres, MySQL, HSQL, SQLite, Remote SPARQL endpoints, Local in-memory triplestores) as well as a native in-memory representation (2) data-integration capabilities for exploiting federated heterogeneous data-sources through mappings able to target systems such as SQL, RDF, and black-box (eg. Web-APIs) (3) algorithms for query-answering over heterogeneous and federated data based on query rewriting and/or forward chaining (or chase)

InteGraal has been designed in a modular way, in order to facilitate software reuse and extension. It should make it easy to test new scenarios and techniques, in particular by combining algorithms. The main features of Graal are currently the following: (1) internal storage to store data by using a SQL or RDF representation (Postgres, MySQL, HSQL, SQLite, Remote SPARQL endpoints, Local in-memory triplestores) as well as a native in-memory representation (2) data-integration capabilities for exploiting federated heterogeneous data-sources through mappings able to target systems such as SQL, RDF, and black-box (eg. Web-APIs) (3) algorithms for query-answering over heterogeneous and federated data based on query rewriting and/or forward chaining (or chase)

The RepeatCheckUntilVerified Utility offers a robust asynchronous mechanism for performing repeated condition checks with configurable retries, initial delays, and optional backup actions. Designed for Kotlin-based applications, it simplifies the implementation of retry logic for operations that require validation through repeated attempts, such as network requests, file I/O operations, or any condition-dependent tasks. With features like adjustable timing, maximum attempt control, and failure management through backup actions, it ensures resilience and flexibility in handling operations that may not succeed on the first try. Ideal for applications requiring high reliability and precise control over task verification processes.