Showing 4 open source projects for "data analysis free"

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  • 1
    Apache Doris

    Apache Doris

    MPP-based interactive SQL data warehousing for reporting and analysis

    Apache Doris is a modern MPP analytical database product. It can provide sub-second queries and efficient real-time data analysis. With it's distributed architecture, up to 10PB level datasets will be well supported and easy to operate. Apache Doris can meet various data analysis demands, including history data reports, real-time data analysis, interactive data analysis, and exploratory data analysis. Make your data analysis easier! Support standard SQL language, compatible with MySQL protocol. ...
    Downloads: 0 This Week
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  • 2
    Nebula Graph

    Nebula Graph

    A distributed, fast open-source graph database

    The graph database built for super large-scale graphs with milliseconds of latency. Optimized SUBGRAPH and FIND PATH for better performance. Optimized query paths to reduce redundant paths and time complexity. Optimized the method to get properties for better performance of MATCH statements. Nebula Graph adopts the Apache 2.0 license, one of the most permissive free software licenses in the world. Free as in freedom, because, under the Apache 2.0 license, you can use, copy, modify and...
    Downloads: 5 This Week
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  • 3
    qvge

    qvge

    Qt Visual Graph Editor

    ...Its main goal is to make possible visually edit two-dimensional graphs in a simple and intuitive way. Please note that qvge is not a replacement for such a software like Gephi, Graphvis, Dot, yEd, Dia and so on. It is neither a tool for "big data analysis" nor a math application. It is really just a simple graph editor :)
    Downloads: 8 This Week
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  • 4
    PANDA

    PANDA

    A comprehensive and flexible quantification tool for proteomics data

    PANDA is a comprehensive and flexib tool for quantitative proteomics data analysis, which is developed based on our solid foundations in quantitative proteomics for years. Several novelties have been implemented in it. First, we implement the advantage algorithms of LFQuant (Proteomics 2012, 12, (23-24), 3475-84) and SILVER (Bioinformatics 2014, 30, (4), 586-7) into PANDA. Second, we consider the state-of-art concept of quantification reliability in this quantitative workflow. ...
    Downloads: 0 This Week
    Last Update:
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