Search Results for "data collection algorithm"

Showing 4 open source projects for "data collection algorithm"

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  • 1
    X's Recommendation Algorithm

    X's Recommendation Algorithm

    Source code for the X Recommendation Algorithm

    The Algorithm is Twitter’s open source release of the core ranking system that powers the platform’s home timeline. It provides transparency into how tweets are selected, prioritized, and surfaced to users, reflecting Twitter’s move toward openness in recommendation algorithms. The repository contains the recommendation pipeline, which incorporates signals such as engagement, relevance, and content features, and demonstrates how they combine to form ranked outputs. Written primarily in Scala...
    Downloads: 0 This Week
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  • 2
    FiloDB

    FiloDB

    Distributed Prometheus time series database

    ..., with distributed querying built in. Support for indexing and fast querying over flexible tags for each time series/partition, just like Prometheus. Holds a huge amount of data in-memory thanks to columnar compression techniques. Designed for highly concurrent, low-latency workloads such as dashboards and alerting. Data immediately available for querying once ingested.
    Downloads: 1 This Week
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  • 3
    DISTOD

    DISTOD

    Distributed discovery of bidirectional order dependencies

    The DISTOD data profiling algorithm is a distributed algorithm to discover bidirectional order dependencies (in set-based form) from relational data. DISTOD is based on the single-threaded FASTOD-BID algorithm [1], but DISTOD scales elastically to many machines outperforming FASTOD-BID by up to orders of magnitude. Bidirectional order dependencies (bODs) capture order relationships between lists of attributes in a relational table. They can express that, for example, sorting books...
    Downloads: 0 This Week
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  • 4
    PredictionIO

    PredictionIO

    Machine learning server for building predictive applications

    Apache PredictionIO is an open-source machine learning server designed to simplify the process of building and deploying predictive engines. It offers a scalable infrastructure with support for multiple ML algorithms, event data collection, and deployment workflows. Developers can use templates or build custom engines, making it a flexible solution for integrating machine learning into applications.
    Downloads: 0 This Week
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