Browse free open source C Big Data Tools and projects below. Use the toggles on the left to filter open source C Big Data Tools by OS, license, language, programming language, and project status.

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  • 1
    Logan

    Logan

    Logan is a lightweight case logging system based on mobile platform

    Logan is a log platform with the ability to collect, store, upload and analyze front-end logs. We provide five components, including iOS SDK, Android SDK, Web SDK, analysis services Server SDK and LoganSite. In addition, we also provide a Flutter plugin Flutter Plugin. LoganSite provides a visualized way for developers to scan and search logs uploaded from App and Web. To put it simply, the traditional idea is to piece together the problems that appear in the logs of each system, but the new idea is to aggregate and analyze all the logs generated by the user to find the scenes with problems. In the future, we will provide a data platform based on Logan big data, including advanced functions such as machine learning, troubleshooting log solution, and big data feature analysis.
    Downloads: 2 This Week
    Last Update:
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  • 2
    FastoRedis

    FastoRedis

    Cross-platform open source Redis DB management tool

    FastoRedis (fork of FastoNoSQL) — is a cross-platform open source Redis management tool (i.e. Admin GUI). It put the same engine that powers Redis's redis-cli shell. Everything you can write in redis-cli shell — you can write in FastoRedis! Our program works on the most amount of Linux systems, also on Windows, Mac OS X, FreeBSD and Android platforms, on desktops and embedded devices.
    Downloads: 5 This Week
    Last Update:
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  • 3

    Faum

    Fast Autonomous Unsupervised Multidimiensional Classification

    This is the proof-of-concept implementation of the FAUM Clustering method. This implementation was used to perform the published results and is now released in the hope that it will be useful.
    Downloads: 0 This Week
    Last Update:
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  • 4

    Random Bits Forest

    RBF: a Strong Classifier/Regressor for Big Data

    We present a classification and regression algorithm called Random Bits Forest (RBF). RBF integrates neural network (for depth), boosting (for wideness) and random forest (for accuracy). It first generates and selects ~10,000 small three-layer threshold random neural networks as basis by gradient boosting scheme. These binary basis are then feed into a modified random forest algorithm to obtain predictions. In conclusion, RBF is a novel framework that performs strongly especially on data with large size.
    Downloads: 0 This Week
    Last Update:
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