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Awesome GitHub RepositoriesInformation Models

Hierarchical data structures using object, variable, and reference types to represent real-world assets.

Distinct from User-Defined Types: None of the candidates describe industrial information modeling; they focus on database row models or programming language types.

Explore 2 awesome GitHub repositories matching data & databases · Information Models. Refine with filters or upvote what's useful.

Awesome Information Models GitHub Repositories

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  • irs-public/direct-fileIRS-Public 的头像

    IRS-Public/direct-file

    4,583在 GitHub 上查看↗

    Direct File 是一个联邦税务申报应用,旨在收集纳税人信息并将联邦税表传输到政府 API。它作为一个税务数据导出工具,支持通过 XML 或 JSON 将联邦申报数据传输到第三方州和地方税务软件。 该系统利用声明式知识图谱来管理跨客户端和服务器环境的复杂业务规则和部分数据集。 该应用采用访谈驱动的流程来收集信息,并使用基于模式的导出系统进行税务数据交换。它集成了数据存储和同步,以对不完整信息进行建模,并验证跨环境的业务规则。

    Uses a knowledge graph to reason about partial data sets and manage business rules across environments.

    JavaScript
    在 GitHub 上查看↗4,583
  • open62541/open62541open62541 的头像

    open62541/open62541

    3,033在 GitHub 上查看↗

    open62541 is an open-source implementation of the OPC Unified Architecture standard, providing a complete stack for industrial automation and data exchange. It functions as both an OPC UA server and client, enabling the creation of an object-oriented address space to model physical industrial assets and their relationships. The project is designed for versatility across diverse hardware, offering a C99 library that can be deployed to both cloud servers and resource-constrained embedded environments. It distinguishes itself through a pluggable network layer and interface-based security backend

    Provides an object-oriented system for defining industrial information models and asset relationships.

    Ccclientiec-62541
    在 GitHub 上查看↗3,033
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探索子标签

  • Partial Data ReasoningUsing structured models to reason about and validate incomplete or partial data sets. **Distinct from Information Models:** Distinct from Information Models: focuses specifically on the ability to handle and reason with missing or incomplete data sets.