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Moreover Provider

A Spark based ETL tool

The JAMES core module is part of the JAMES framework for discrete optimization using local search metaheuristics in Java. The core contains many general components for both problem specification and search application. It includes a wide range of generic optimization algorithms to solve the specified problems. Moreover, the core provides implementations of specific components for subset selection.

Provides all necessary resources for a Java project to enforce the coding style that I am using in my lectures about software development at Munich University of Applied Sciences and in all of my open source projects. It configures several static analysis tools for Maven and IntelliJ. Moreover, it provides some sample classes that already use this style guide. This classes can be used as such but are not required in this project. These classes also use some additional libraries that are included using the Maven dependency mechanism. If the sample classes are deleted then the dependencies can be safely deleted, too.

This small library provides an optimized solution to a problem where tasks for a particular key need to be processed sequentially as they arrive. This kind of problem can be solved by a SingleThreadExecutor, however it is not efficient. The issue is that the tasks for unrelated keys are not being processed in parallel, instead they are put into a queue common to all keys and wait for the single thread to execute them. This library allows them to be executed concurrently. Moreover this library works well in a situation where all the possible keys and their number is not known upfront.

GroupDocs.Annotation lets you add notes to PDF and Word documents, as well as to image files – all directly from a web browser. It is a convenient web-based tool that doesn’t require any software installation and allows you and your colleagues to annotate documents online. Moreover, with GroupDocs.Annotation, you can add your notes to a document and then send it for approval or review, or share the document with others for online collaborative review in real-time. This way you get feedback faster and can keep everyone’s notes and comments in a single file.

Reco4j is an open source project aims at developing a recommendation framework based on graph data sources. We choose graph databases for several reasons. They are NoSQL databases, so "schemaless". This means that it is possible to extend the basic data structure with intermediate information, i.e. similarity value between item and so on. Moreover, since every information is expressed with properties, nodes and relations, the recommendation process can be customized to work on every graph. Reco4j can be used on every graph where "user" and "item" is represented by node and the preferences are modelled as relationship between them. Current implementation leverage on Neo4j as first graph database integrated in our framework.

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)