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This module contains a classpath scanner, and bytecode reader. It is useful in any java runtime environment, and allows java code to parse bytecode into a meta structure of classes, annotations, methods and fields. This meta structure is used throughout xapi to allow us to extend java structures in a cross-platform compliant way (exposing services and service objects so you can hack support for whatever runtime you need). It is especially useful for fast runtime injection, and as an extensible structure during codegen (which exposes mutable interfaces for our meta structure)

snmpAdaptor4j is an adaptor for Java Management eXtensions (JMX) providing a simple access to MBeans via the SNMP protocol. Thus, this adapter you allow to connect most monitoring tools (like Nagios and Cacti) to your Java applications. For each MBean, an XML mapping file allows to establish the relationships between attributes and the MIB of the SNMP adapter. No additional code is necessary to integrate the MBeans in the SNMP protocol. This adapter can work in a multi-applications environment (such as application servers).

This module was created to fix issue DIRSHARED-91 where the embedded Felix instance inside the default LdapCodecService implementation was making it very problematic to deploy the ldap-codec inside an RCP (OSGi) environment and hence Apache Directory Studio could not use it. This module is most likely temporary and will disappear once we are fully OSGi enabled. This module is a plain old jar, not a bundle to avoid accidental reuse as an OSGi module because it contains the version of the LdapCodecService that embeds Felix to make controls and extended ops pluggable in the codec.

This plugin will support various tasks within the Weblogic 8.1, 9.x, 10.x and 12.x environment. This version starts to support weblogic 10 and 11. The mojo supports such tasks as deploy, undeploy,clientgen,servicegen, and appc are supported as well as many others. The plugin uses exposed API's that are subject to change but have been tested in 8.1 SP 4-6 and 9.0 - 9.2 MP3, 10.x. There are two versions of the plugin to support the two environments based on differences in the JDK. The 9.x version is currently being refactored to support the standard JSR supported deployment interface. The code used in the plugin has been contributed to the Cargo project however to date has not be integrated into the codebase. Please feel free to suggest improvements or help support this plugin effort.

The Jentity framework attempts to provide the Java platform with more powerful building blocks for integrating objects and components. The framework focuses on the challenge of maintaining consistent data or state in a dynamic environment. This is achieved by provide reusable, extensible objects, that can use to construct higher order structures like components, applications and systems. Examples of concrete problems that can be effectively solved by using the Jentity framework, are shared models, distributed models, MVC constructs.

The Fuzzy Lattice Reasoning Classifier uses the notion of Fuzzy Lattices for creating a Reasoning Environment. The current version can be used for classification using numeric predictors. For more information see: I. N. Athanasiadis, V. G. Kaburlasos, P. A. Mitkas, V. Petridis: Applying Machine Learning Techniques on Air Quality Data for Real-Time Decision Support. In: 1st Intl. NAISO Symposium on Information Technologies in Environmental Engineering (ITEE-2003), Gdansk, Poland, 2003; V. G. Kaburlasos, I. N. Athanasiadis, P. A. Mitkas, V. Petridis (2003). Fuzzy Lattice Reasoning (FLR) Classifier and its Application on Improved Estimation of Ambient Ozone Concentration.

A bank decides using various criteria whether a customer wil receive a loan. An insurer uses specific information to determine which insurance product is most suitable for its customer. A municipality makes decisions based on yet other information, to determine whether it will grant permits or if a citizen is entitled to a public transport pass. Decision trees are frequently used in order to make such decisions or recommendations. Albero is a new generation decision tree solution. It is simple, fast, and flexible. Further, Albero is easily adaptable and can integrate easily into any environment.