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FIWARE Data Models

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This repository is going to be archived (Jan-2020) and subsequent works will be carried out in [Smart data models] ( repositories. Further questions on migration. [email protected]

This repository contains:

  • JSON Schemas and documentation on harmonized datamodels for different Smart Domains, particularly Smart Cities and Smart Agrifood.
  • code that allows to expose different harmonized datasets useful for different applications. Such datasets are currently exposed through the FIWARE NGSI version 2 and/or NGSI-LD APIs (query).

This work is aligned with the results of the GSMA IoT Big Data Project. Such project is working on the harmonization of APIs and data models for fueling IoT and Big Data Ecosystems. In fact the FIWARE data models are a superset of the GSMA Data Models.

Some of the Data Models in this Repository have been adopted by a joint initiative between TM Forum and FIWARE Foundation. For more info please check

📚 Documentation

Data Models adoption

To support the adoption, we created a short guideline for the usage of data models. If you are using NGSI-LD, you should also check the NGSI-LD HowTo and the NGSI-LD FAQ.

JSON Schemas

A JSON Schema is provided for every harmonized data model. In the future all the documentation could be generated from a JSON Schema, as it is part of our roadmap. The different JSON Schemas usually depend on common JSON Schema definitions found at the root directory of this repository.

There are different online JSON Schema Validators, for instance: For the development of these schemas the AJV JSON Schema Validator is being used. For using it just install it through npm:

    npm install ajv
    npm install ajv-cli

A script is provided for convenience.

Note: JSON Schemas capture the name and data type of each Entity Attribute. For instance, this means that to test JSON schema examples with a FIWARE NGSI version 2 or NGSI-LD API implementation, you need to use the keyValues mode (options=keyValues).

How to contribute

Contributions should come in the form of pull requests.

Please note that some of the Data Models in this Repository have been adopted by a joint initiative between TM Forum and FIWARE Foundation. Pull Requests for those Data Models shall be done against the corresponding Repository at the Organization

New data models should be added under a folder structured as follows:

The name of the folder should match the Entity Type used in the JSON Schema (e.g. NewModel). For data models including more entities, a hierarchical folder should be used. The parent folder can include common JSON schemas shared among the entities. e.g.:

  • specs/
    • NewModel/
      • doc/
      • newmodel-schema.json: the common schema for the different entities.
      • NewModelEntityOne/
        • doc/
        • schema.json
        • example.json
        • example-normalized.json
        • example-normalized-ld.jsonld
      • NewModelEntityTwo/
        • doc/
        • schema.json
        • example.json
        • example-normalized.json
        • example-normalized-ld.jsonld

To facilitate contributions and their validation, we developed a tool that is also used for the Continuous Integration of FIWARE Data Models. The FIWARE Data Model validator checks the adherence of each data model to the FIWARE Data Models guidelines.

For using it just install it through npm:

npm install -g fiware-model-validator

More details are available in the validator documentation.

Formatting and Text Correction

When creating a Pull Request, please ensure the files are properly formatted, the Husky should do this automatically on git commit, but the files can be manually formatted at any time using the prettier and prettier:text commands:

cd validator
npm i

# To format JavaScript files:
npm run prettier

# To format Markdown files:
npm run prettier:text

# To Auto-correct Markdown files:
npm run lint:text

To check for spelling mistakes and dead links in the text within the Data Model, run the text linter as shown:

npm test

Related Projects




MIT © 2019 FIWARE Foundation e.V.

License: MIT

All the code in this repository is licensed under the MIT License. However each original data source may have a different license. So before using harmonized data please check carefully each data license.


License: CC BY 4.0

All the data models documented here are offered under a Creative Commons by Attribution 4.0 License.

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