Download JAR files tagged by relational with all dependencies

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.

eFaps is a framework used to map objects with or without attached files to a relational database and optional file systems (only for attaches files). Configurable access control can be provided down to object and attribute level depending on implementation and use case. Depending on requirements, events (like triggers) allow to implement business logic and to separate business logic from user interface. The framework includes integrations (e.g. webdav, full text search) and a web application as 'simple' configurable user interface. Some best practises, example web application modules (e.g. team work module) support administrators and implementers using this framework.

Siena is a persistence abstraction layer made in Java with various implementations: * siena-jdbc: persists objects into a relational database using JDBC (status: deeply tested) * siena-gae: persists objects into the Google App Engine's datastore using its low-level API (status: beta) * siena-sdb: persists objects using Amazon's SimpleDB (very experimental) * siena-remote: persists objects remotely using a remote web application that uses siena. (planning) Siena is also: * Intrusive. You make a class persistent by extending a certain class. Relationships are defined also in an intrusive way. * Simple. Simplicity is the most important thing. * Limited. Due to the simplicity there are several unsupported things such as JOINs. * Extensible. There are two interfaces you can implement on your own way.

Shades is an Object/Relational Mapping (ORM) framework that works in conjunction with JDBC APIs. Instead of relying on XML or annotations for configuration, Shades relies on an interface called ORMapping. This makes shades simple and flexible. Shades has no new transaction model nor Connection management which you must learn. You control the transactions, and the Connection that Shades uses, through plain JDBC interfaces. Shades is designed to work well with modern web development frameworks like Tapestry and Wicket.

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.

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).

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 actions 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 and http://sourceforge.net/p/dbvolution/blog/

The core of the Struts framework is a flexible control layer based on standard technologies like Java Servlets, JavaBeans, ResourceBundles, and Extensible Markup Language (XML), as well as various Jakarta Commons packages. Struts encourages application architectures based on the Model 2 approach, a variation of the classic Model-View-Controller (MVC) design paradigm. Struts provides its own Controller component and integrates with other technologies to provide the Model and the View. For the Model, Struts can interact with any standard data access technology, including Enterprise Java Beans, JDBC, and Object Relational Bridge. For the View, Struts works well with JavaServer Pages, including JSTL and JSF, as well as Velocity Templates, XSLT, and other presentation systems. The Struts framework provides the invisible underpinnings every professional web application needs to survive. Struts helps you create an extensible development environment for your application, based on published standards and proven design patterns.

This is an implementation of the Neural Gas algorithm on distance data (Relational Neural Gas) for unsupervised clustering. We recommend that you use the functions provided by the RelationalNeuralGas class for your purposes. All other classes and functions are utilities which are used by this central class. In particular, you can use RelationalNeuralGas.train() to obtain a RNGModel (i.e. a clustering of your data), and subsequently you can use RelationalNeuralGas.getAssignments() to obtain the resulting cluster assignments, and RelationalNeuralGas.classify() to cluster new points which are not part of the training data set. The underlying scientific work is summarized nicely in the dissertation "Topographic Mapping of Dissimilarity Datasets" by Alexander Hasenfuss (2009). The basic properties of an Relational Neural Gas algorithm are the following: 1.) It is relational: The data is represented only in terms of a pairwise distance matrix. 2.) It is a clustering method: The algorithm provides a clustering model, that is: After calculation, each data point should be assigned to a cluster (for this package here we only consider hard clustering, that is: each data point is assigned to exactly one cluster). 3.) It is a vector quantization method: Each cluster corresponds to a prototype, which is in the center of the cluster and data points are assigned to the cluster if and only if they are closest to this particular prototype. 4.) It is rank-based: The updates of the prototypes depend only on the distance ranking, not on the absolute value of the distances.