awesome-repositories.com
博客
MCP
awesome-repositories.com

通过 AI 驱动的搜索,发现最优秀的开源仓库。

探索精选搜索开源替代品自托管软件博客网站地图
项目MCP 服务器关于排名机制媒体报道
法律隐私政策服务条款
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

5 个仓库

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

用 AI 发现最棒的仓库。我们将通过 AI 为您搜索最匹配的仓库。
  • redis/redisinsightredis 的头像

    redis/RedisInsight

    8,556在 GitHub 上查看↗

    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
    在 GitHub 上查看↗8,556
  • goproxy/goproxy.cngoproxy 的头像

    goproxy/goproxy.cn

    7,082在 GitHub 上查看↗

    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
    在 GitHub 上查看↗7,082
  • nytimes/covid-19-datanytimes 的头像

    nytimes/covid-19-data

    6,970在 GitHub 上查看↗

    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
    在 GitHub 上查看↗6,970
  • dgraph-io/ristrettodgraph-io 的头像

    dgraph-io/ristretto

    6,932在 GitHub 上查看↗

    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
    在 GitHub 上查看↗6,932
  • water8394/flink-recommandsystem-demowater8394 的头像

    water8394/flink-recommandSystem-demo

    4,473在 GitHub 上查看↗

    This project is a real-time product recommendation engine built on Apache Flink. It functions as a streaming behavioral analytics pipeline that processes raw logs to derive user interests and product popularity trends. The system utilizes a collaborative filtering engine to compute item similarity via cosine similarity and shared user interaction patterns. It employs a hybrid re-ranking pipeline that combines global popularity lists with personalized user profiles to sort recommended items. The architecture incorporates a wide-column user store using HBase for persistent behavioral records a

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

    Javaflinkflink-examplesflink-hbase
    在 GitHub 上查看↗4,473
  1. Home
  2. Data & Databases
  3. Trending Item Tracking