awesome-repositories.com
Blog
awesome-repositories.com

Entdecke die besten Open-Source-Repositories mit KI-gestützter Suche.

EntdeckenKuratierte SuchenOpen-Source-AlternativenSelf-hosted SoftwareBlogSitemap
ProjektÜber unsRanking-MethodikPresseMCP-Server
RechtlichesDatenschutzAGB
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

2 Repos

Awesome GitHub RepositoriesSynchronized View Filtering

Mechanisms that coordinate filtering state across multiple independent visualization panels.

Distinct from Data-View Synchronizers: Unlike Data-View Synchronizers, this specifically handles the coordination of subset selection across different visual projections.

Explore 2 awesome GitHub repositories matching user interface & experience · Synchronized View Filtering. Refine with filters or upvote what's useful.

Awesome Synchronized View Filtering GitHub Repositories

Finde die besten Repos mit KI.Wir suchen mit KI nach den am besten passenden Repositories.
  • perspective-dev/perspectiveAvatar von perspective-dev

    perspective-dev/perspective

    10,981Auf GitHub ansehen↗

    Perspective is a columnar data analytics engine and high-performance visualization component powered by WebAssembly. It provides a system for analyzing and visualizing large or streaming datasets through interactive data grids and charts, utilizing a compiled binary to achieve near-native performance within the browser. The project distinguishes itself through a WebSocket-based data streaming interface and deep Apache Arrow integration, which minimize memory overhead when synchronizing tables between servers and clients. It acts as a remote query proxy capable of translating visualization con

    Implements mechanisms to coordinate filtering state across multiple independent visualization panels.

    C++analyticsbidata-visualization
    Auf GitHub ansehen↗10,981
  • pair-code/facetsAvatar von PAIR-code

    PAIR-code/facets

    7,340Auf GitHub ansehen↗

    Facets is a set of interactive software tools for the statistical analysis, distribution visualization, and multidimensional exploration of machine learning datasets. It provides a visual interface for identifying outliers and missing values in numeric and string data, specifically designed for auditing dataset quality and identifying skews between training and validation sets. The system uses multidimensional facet-based visualization and interactive bucketing to map individual data points across multiple feature axes. It employs synchronized view filtering and animated dimension transitions

    Updates all active visualization panels simultaneously when a specific data subset is selected in any single view.

    Jupyter Notebook
    Auf GitHub ansehen↗7,340
  1. Home
  2. User Interface & Experience
  3. Synchronized View Filtering