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Showing 4 open source projects for "java mail server"

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  • MongoDB Atlas runs apps anywhere Icon
    MongoDB Atlas runs apps anywhere

    Deploy in 115+ regions with the modern database for every enterprise.

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  • 1
    Sesame

    Sesame

    Java RDF Framework

    This project is no longer actively maintained. It is succeeded by the Eclipse RDF4J project, which can be found at GitHub and at http://www.rdf4j.org/. Sesame is a de-facto standard framework for processing RDF data. This includes parsing, scalable storage, reasoning and full SPARQL 1.1 query/update support. Sesame offers a fully modular toolkit and an easy-to-use Java API that can be connected to all leading RDF storage solutions.
    16 Reviews
    Downloads: 60 This Week
    Last Update:
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  • 2

    RDF Content Provider for iQser GIN

    Plugin to connect RDF sources with the GIN Server

    GIN Server is a semantic middleware for easy data integration and automized analysis. The extendable architecture allows to plugin in data sources, analytics and event handling. This RDF Content Provider enables access to Semantic Web Content as an RDF file or SPAEQL endpoint.
    Downloads: 0 This Week
    Last Update:
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  • 3
    SemaRule Navigator
    SemaRule Navigator is an Integrated Suite of Open-Source and Free-License Software, placing Semantic and Text Analysis Technologies in the toolbox of Researchers, Students, and Enterprises.
    Downloads: 0 This Week
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  • 4
    RuDolFWS (a.k.a. RDFWS) is a web service that lets you add, modify, remove and manage ontologies in a remote Sesame repository server.
    Downloads: 0 This Week
    Last Update:
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  • Cloud data warehouse to power your data-driven innovation Icon
    Cloud data warehouse to power your data-driven innovation

    BigQuery is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data.

    BigQuery Studio provides a single, unified interface for all data practitioners of various coding skills to simplify analytics workflows from data ingestion and preparation to data exploration and visualization to ML model creation and use. It also allows you to use simple SQL to access Vertex AI foundational models directly inside BigQuery for text processing tasks, such as sentiment analysis, entity extraction, and many more without having to deal with specialized models.
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