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5 repository-uri

Awesome GitHub RepositoriesTrending Item Tracking

Memory-efficient probabilistic structures used to identify the most frequent items within a dataset.

Distinguishing note: Existing candidates focus on shipping logistics, performance trends, or simple item management, not probabilistic frequency estimation.

Explore 5 awesome GitHub repositories matching data & databases · Trending Item Tracking. Refine with filters or upvote what's useful.

Awesome Trending Item Tracking GitHub Repositories

Găsește cele mai bune repo-uri cu AI.Vom căuta cele mai potrivite repository-uri folosind AI.
  • redis/redisinsightAvatar redis

    redis/RedisInsight

    8,556Vezi pe GitHub↗

    RedisInsight is a graphical user interface and management tool for browsing, analyzing, and administering Redis databases. It provides a visual environment for exploring key-value data structures, managing database instances, and performing data analysis across different operating systems and deployments. The tool distinguishes itself by providing dedicated visual managers for complex operations, including a vector database manager for configuring embeddings and similarity searches, a query workbench for executing raw commands and Lua scripts, and a performance monitoring dashboard for tracki

    Identifies the most frequent items in a dataset using memory-efficient probabilistic structures.

    TypeScriptdatabase-guiredisredis-gui
    Vezi pe GitHub↗8,556
  • goproxy/goproxy.cnAvatar goproxy

    goproxy/goproxy.cn

    7,082Vezi pe GitHub↗

    goproxy.cn is a Go module proxy and checksum database proxy designed to manage the availability and integrity of Go software modules. It provides a regional mirror of modules to ensure reliable dependency downloads for build pipelines and CI/CD optimization. The service utilizes a content delivery network for module mirroring and distribution to reduce latency. It employs a lazy-loading proxy cache that retrieves and stores modules from primary sources on demand to optimize storage. The platform includes software download analytics to track version-specific download counts and usage trends.

    Identifies the most active software modules over specific timeframes to monitor popularity.

    HTMLchinagogoproxy
    Vezi pe GitHub↗7,082
  • nytimes/covid-19-dataAvatar nytimes

    nytimes/covid-19-data

    6,970Vezi pe GitHub↗

    This project is a public health dataset providing historical and real-time COVID-19 case and death counts across the United States. It consists of a collection of CSV files containing time-series pandemic data organized by date, state, and county. The dataset includes specialized records for institutional outbreaks, tracking infection and death rates within correctional facilities, colleges, and universities. It also provides statistics on excess mortality to estimate total pandemic impact and survey-based data on mask usage prevalence across different counties. To facilitate geographic anal

    Tracks the frequency of mask usage across counties using survey data and demographic weighting.

    covid-19
    Vezi pe GitHub↗6,970
  • dgraph-io/ristrettoAvatar dgraph-io

    dgraph-io/ristretto

    6,932Vezi pe GitHub↗

    Ristretto is a high-performance in-memory cache and concurrent key-value store for Go applications. It provides a thread-safe memory store that manages strict memory bounds and employs probabilistic set filters to reduce lookup overhead. The system is distinguished by an admission-policy cache that utilizes frequency sketches and cost-based eviction to maximize hit ratios. It minimizes contention and improves throughput through the use of striped ring buffers and concurrent map sharding. The project covers a broad range of data management capabilities, including time-based expiration, item f

    Uses memory-efficient probabilistic structures to identify and track the most frequent items within the cache.

    Go
    Vezi pe GitHub↗6,932
  • water8394/flink-recommandsystem-demoAvatar water8394

    water8394/flink-recommandSystem-demo

    4,473Vezi pe GitHub↗

    Acest proiect este un motor de recomandare a produselor în timp real construit pe Apache Flink. Acesta funcționează ca un pipeline de analiză comportamentală prin streaming care procesează log-urile brute pentru a deriva interesele utilizatorilor și tendințele de popularitate a produselor. Sistemul utilizează un motor de filtrare colaborativă pentru a calcula similaritatea articolelor prin similaritatea cosinus și modelele de interacțiune partajate ale utilizatorilor. Utilizează un pipeline hibrid de re-ranking care combină listele de popularitate globală cu profilurile personalizate ale utilizatorilor pentru a sorta articolele recomandate. Arhitectura încorporează un magazin de utilizatori wide-column folosind HBase pentru înregistrări comportamentale persistente și un cache susținut de Redis pentru listele de popularitate a articolelor în timp real. Pipeline-ul include capabilități pentru extragerea intereselor comportamentale, analiza intervalelor de interacțiune și un dashboard de performanță pentru monitorizarea ratelor de ingestie a log-urilor.

    Tracks real-time item trends using time windows and efficient frequency estimation.

    Javaflinkflink-examplesflink-hbase
    Vezi pe GitHub↗4,473
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