Overview

The tool automates the generation of Smart Data Models (SDM) from visual models, bridging the gap between high-level domain design and technical implementation for Digital Twins and IoT ecosystems.

Technical Workflow

1. Input: Users define domain entities and relationships using B-UML (a simplified UML dialect) within the BESSER Pearl editor.

2. Transformation: The engine maps these models to the NGSI-LD standard and Schema.org vocabularies.

3. Output: For every entity, it automatically generates a compliant folder containing:

   • schema.json: The technical JSON Schema definition.

   • Examples: Multi-format payloads including JSON-LD, NGSI-v2, and Normalized NGSI-LD.

   • Documentation: Automatically derived human-readable specifications.

Key Advantages

• Interoperability: Ensures 100% compliance with ETSI NGSI-LD and SDM contribution guidelines.

• Model-Driven Engineering (MDE): Moves the “source of truth” to a visual model, reducing manual coding errors in complex JSON-LD structures.

• Efficiency: Accelerates the deployment of standardized data spaces by automating the boilerplate required for context brokers (e.g., Orion).

Calling all SDM contributors!

To ensure a smooth integration process, we want to remind everyone that all contributions undergo automated testing prior to submission. To help you streamline your workflow and catch potential issues early, we’ve made our testing suite available for local use.

🛠️ Test Locally, Submit with Confidence

You can access the full source code for our testing framework under an open-source license. Running these tests on your own machine not only boosts the quality of your code but also saves valuable time for both you and the maintainers.

Getting Started

1. Download the Source: Head over to our repository to grab the testing suite.

2. Configure: Follow the simple instructions in the README. (Quick tip: You only need to update config.json with your specific local directories).

3. Run Anywhere: The suite is flexible—it supports testing against both remote repositories and local file systems.

By validating your work locally before you push, you’re helping us maintain a high standard for the SDM project.

Happy coding!

List of tests

• test_array_object_structure.py
• test_duplicated_attributes.py
• test_file_exists.py
• test_name_attributes.py
• test_schema_descriptions.py
• test_schema_metadata.py
• test_string_incorrect.py
• test_valid_json.py
• test_valid_keyvalues_examples.py
• test_valid_ngsild.py
• test_valid_ngsiv2.py
• test_yaml_files.py

The contributors of new data models can test their data models in their local repositories with the source code of the testing tool than also can use online

Home -> tools -> test your data model

it has been updated to deal with those attributes coming from languageMap properties in NGSI

In the data-models repository you can access to the first version to use smart data models as a service. Thanks to the works for the Cyclops project.

The files available create a wrap up around pysmartdatamodels package and also add one service for the online validation of NGSI-LD payloads.

Here is the readme contents to have an explanation of this first version

This project consists of two main components:

1. A FastAPI-based web service (pysdm_api3.py) that provides access to Smart Data Models functionality
2. A demo script (demo_script2.py) that demonstrates the API endpoints
3. A requirements file for the components use
4. A bash script to run it

A RESTful API that interfaces with the pysmartdatamodels library to provide access to Smart Data Models functionality.

• Payload Validation: Validate JSON payloads against Smart Data Models schemas
• Data Model Exploration: Browse subjects, data models, and their attributes
• Search Functionality: Find data models by exact or approximate name matching
• Context Retrieval: Get @context information for data models
• if you need other please email us to info @ smartdatamodels. org

The /validate-url endpoint performs comprehensive validation:

1. Fetches JSON from the provided URL
2. Normalizes NGSI-LD payloads to key-values format
3. Extracts the payload type
4. Finds all subjects containing this type
5. Retrieves schemas for each subject
6. Validates against all schemas
7. Returns consolidated results

A simple interactive script that demonstrates the API endpoints by opening a series of pre-configured URLs in your default web browser.

• Opens each URL in a new browser tab
• Pauses between URLs for user input
• Allows early termination with ‘exit’ command
• Provides clear progress indicators

1. Configure the URLs in the my_web_urls list
2. Run the script: python demo_script2.py
3. Follow the on-screen instructions

The demo includes examples of:

• Payload validation
• Subject listing
• Data model exploration
• Attribute retrieval
• Example payloads
• Search functionality
• Exact matching
• Context retrieval

• Python 3.7+
• FastAPI
• Uvicorn
• httpx
• pydantic
• jsonschema
• webbrowser (standard library)

pip install fastapi uvicorn httpx pydantic jsonschema

python pysdm_api3.py

python demo_script2.py

Edit the my_web_urls list in demo_script2.py to change which endpoints are demonstrated.

Apache 2.0