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This project is meant to simplyfy the usage of the excelent Configuration Admin and MetaType services for Felix OSGi. The bundle will create a service that allows other bundles to register an annotated interface. The ManagedProperties service will then create all the Metatype information, as well as ensuring that objects are cast to their right types. ManagedProperties also has a feature called Filters. A Filter is used to create an object, say a File, directly from a primitive Configuration Admin type. It can also contain logic, to allow the configuration to fail if, for example, a File doesn't exist.

This is a plugin to Madura Objects. It provides a rules/constraint engine to assist with validation, deriving new values from user inputs (eg total of invoices entered on this customer) an manipulating metadata (eg because the amount is above X we make some field readonly). Note that the Java that is using the monitored objects is quite unaware of the rules layer implemented here. That means you can change rules without having to go back to your Java code, and it also means you don't have to wonder if everything implemented the same rules. Anything using that Java object has the rules (unless you turn them all off).

A collection of scala and java classes for some basic character level processing for the Sanskrit and other Indic (kannada, telugu, etc..) languages, contributed by the open source sanskrit-coders projects and friends. Some notable facilities: * Transliterate text from one script or encoding scheme to another. * Some grammar simulation. Examples: see https://github.com/sanskrit-coders/indic-transliteration Contributions and suggestions are invited at https://github.com/sanskrit-coders/indic-transliteration . (Sister projects there may also be of interest.)

ISO 13616-1:2007 specifies the elements of an international bank account number (IBAN) used to facilitate the processing of data internationally in data interchange, in financial environments as well as within and between other industries. The IBAN is designed for automated processing, but can also be used conveniently in other media interchange when appropriate (e.g. paper document exchange, etc.). ISO 13616-1:2007 does not specify internal procedures, file organization techniques, storage media, languages, etc. to be used in its implementation, nor is it designed to facilitate the routing of messages within a network. It is applicable to the textual data which might be conveyed through a system (network). This project provides a general purpose ISO 13616-1:2007 compatible IBAN Java class.

A collection of scala and java classes for some basic natural language processing (NLP) for the Sanskrit language, contributed by the open source SanskritNLP project and friends. Some notable facilities: * Transliterate text from one script or encoding scheme to another. * Deal with babylon dictionaries. * Use bots to write to wiki projects (wiktionary, wikisource etc..). * Basic metre identification. * Some grammar simulation. Contributions and suggestions are invited at https://github.com/sanskrit-coders/sanskritnlpjava . (Sister projects there may also be of interest.)

The RestFixture is a FitNesse (http://fitnesse.org) fixture that allows developers and/or product owners to write test fixtures for REST services with simplicity in mind. The idea is to write tests that are self documenting and easy to write and read, without the need to write Java code. The fixture allows test writers to express tests as actions (any of the allowed HTTP methods) to operate on resource URIs and express expectations on the content of the return code, headers and body. All without writing one single line of Java code. And it also works as a living/executable documentation of the API.

PowerMock allows you to unit test code normally regarded as untestable. For instance it is possible to mock static methods, remove static initializers, allow mocking without dependency injection and more. PowerMock works by bytecode manipulation. PowerMock also contain some utilities that gives you easier access to an objects internal state. PowerMock can be used to test otherwise untestable code and also to achieve a cleaner separation between test and production code.

Information extraction is the process of identifying specified classes of entities, relations, and events in natural language text – creating structured data from unstructured input. JET, the Java Extraction Toolkit, developed at New York University over the past fifteen years, provides a rich set of tools for research and education in information extraction from English text. These include standard language processing tools such as a tokenizer, sentence segmenter, part-of-speech tagger, name tagger, regular-expression pattern matcher, and dependency parser. Also provided are relation and event extractors based on the specifications of the U.S. Government's ACE [Automatic Content Extraction] program. The program is provided under an Apache 2.0 license.

ToroDB is an open source project that turns your RDBMS into a MongoDB-compatible server, supporting the MongoDB query API and MongoDB's replication, but storing your data into a reliable and trusted ACID database. ToroDB currently supports PostgreSQL as a backend, but others will be added in the future. ToroDB natively implements the MongoDB protocol, so you can use it with MongoDB tools and drivers, and features a document-to-relational mapping algorithm that transforms the JSON documents into relational tables. ToroDB also offers a native SQL layer and automatic data normalization and partitioning based on JSON documents' implicit schema.

Welcome to the Blazegraph/TinkerPop3 project. The TP3 implementation has some significant differences from the TP2 version. The data model has been changed to use RDF*, an RDF reification framework described here: https://wiki.blazegraph.com/wiki/index.php/Reification_Done_Right. The concept behind blazegraph-gremlin is that property graph (PG) data can be loaded and accessed via the TinkerPop3 API, but underneath the hood the data will be stored as RDF using the PG data model described in this document. Once PG data has been loaded you can interact with it just like you would interact with ordinary RDF - you can run SPARQL queries or interact with the data via the SAIL API. It just works. The PG data model is also customizable via a round-tripping interface called the BlazeValueFactory, also described in detail in this document.