Download JAR files tagged by logic with all dependencies

With distributed systems being developed as a part of the cloud age. With focus on performance associated with an application dependent on one DB instance, there is a need to bring down the dependency to a database to generate unique numbers for creating a private key when lots of data needs to be stored in the database tables. This library is intended to provide a way of generating unique numbers on the fly in a JAVA based distributed system. This is not a new concept but this library helps in keeping you focused on the business logic that you need to develop for your applications rather than spending time on how this is done. Typically applications powered by RDBMS can leverage the benefit as instant scalability might not be available for these setups when the load to the system spikes.

This is CRUD interface based on standard JEE MVC servlet, JSP, JSTL. It renders forms and lists of any entity according XML settings (Beige-Settings). Database logic is handled by Beige-ORM. It also includes Mail-sender and database replicator through WEB-service. It is SQLite configured. Put WAR file into Tomcat7 webapps, make sure that Tomcat has libraries: HikariCP-2.4.3.jar, sqlite-jdbc-3.8.11.2.jar, slf4j-api-1.7.12.jar (versions may be different), for MS Windows change file separator in META-INF\context.xml connectionURL from "/" to "\" and reassemble WAR. Beigesoft-Accounting-Web extends it into very complex business application.

A collection of filters for manipulating multi-instance data. Includes PropositionalToMultiInstance, MultiInstanceToPropositional, MILESFilter and RELAGGS. For more information see: M.-A. Krogel, S. Wrobel: Facets of Aggregation Approaches to Propositionalization. In: Work-in-Progress Track at the Thirteenth International Conference on Inductive Logic Programming (ILP), 2003. Y. Chen, J. Bi, J.Z. Wang (2006). MILES: Multiple-instance learning via embedded instance selection. IEEE PAMI. 28(12):1931-1947. James Foulds, Eibe Frank: Revisiting multiple-instance learning via embedded instance selection. In: 21st Australasian Joint Conference on Artificial Intelligence, 300-310, 2008.