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The idea and motivation of the lindbergframework is to provide resources and simple solutions to problems based on the concept 'Plug and Use'. In the current version, the framework provides easy and simple solutions to the population of beans from SQL queries, abstraction of the complexities to access to stored procedures and stored functions , population of database cursors, transaction management, dependency injection, integration with Spring, JSF and Adobe Flex. The lindberg-persistence (LINP) solution, implements a important concept: separated repository of SQL commands. Among other resources.

This service implements the Indexer and Searcher interface of WebLab and connect to a remote SOLR engine in order to realize the functions. The connection is mandatory and thus the remote SOLR server should be started beforehand. Configuration of the SOLR server URL is found in src/main/webapp/WEB-INF/cxf-servlet.xml . See in particular "indexerServiceBean" and "searcherServiceBean". Moreover, the service implements several "Analyser" services that could be included in a complete search chain in order to: - enrich a ResultSet with metadata about Hits (see resultSetMetaEnricherServiceBean) - highlight snippet in ResultSet (see highlighterServiceBean) - provide facets related to the current results (see facetSuggestionServiceBean) - suggest spell correction of the original query (see spellSuggestionServiceBean)

Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al. (2007). This implementation globally replaces all missing values and transforms nominal attributes into binary ones. It also normalizes all attributes, so the coefficients in the output are based on the normalized data. Can either minimize the hinge loss (SVM) or log loss (logistic regression). For more information, see S. Shalev-Shwartz, Y. Singer, N. Srebro: Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. In: 24th International Conference on MachineLearning, 807-814, 2007.

relay-jdbc allows a JDBC connection to be relayed through an intermediate relay server: it is both a JDBC driver (client) and a JDBC relay server. It is a fork of https://github.com/AugeoSoftware/VJDBC project, where Aleksei Yu. Semenov has implemented a number of brilliant improvements of the orignal VJDBC project hosted on http://vjdbc.sourceforge.net Original Developers were: Hunter Payne ([email protected]) and Michael Link ([email protected]). For all pre-existing code, credits go to the original respective author. This product brings the original VJDBC concept further and (apart from code quality improvements and bug fixes) implements complete separation of the underlying transport mechanism, introducing support for connecting directly through a SSH pipe, where the relay server interacts with the client through standard I/O pipes. The re-designed transport architecture supports easy development of new transport implementations, allowing this JDBC relay to be quickly adapted to a variety of exotic environments.

Luzzu is a Quality Assessment Framework for Linked Open Datasets. It is a generic framework based on the Dataset Quality Ontology (daQ), allowing users to define their own quality metrics. Luzzu is an integrated platform that: - assesses Linked Data quality using a library of generic and user-provided domain specific quality metrics in a scalable manner; - provides queryable quality metadata on the assessed datasets; - assembles detailed quality reports on assessed datasets. Furthermore, the infrastructure: - scales for the assessment of big datasets; - can be easily extended by the users by creating their custom and domain-specific pluggable metrics, either by employing a novel declarative quality metric specification language or conventional imperative plugins; - employs a comprehensive ontology framework for representing and exchanging all quality related information in the assessment workflow; - implements quality-driven dataset ranking algorithms facilitating use-case driven discovery and retrieval.