Author: maestro

Change of VPS

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Due to the change in the Virtual Private Server in which the site is running some of the backend services in the tools menu are suffering issues.

Please report them to alberto.abella@fiware.org

New data models I4submodelElementCapability and I4SubmodelElementRelationship In AAS subject

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Two new data models have been published in the subject dataModel.AAS (Asset administration shell) in the Smart manufacturing domain.

The data model was created thanks to the contributor Inga Miadowicz

  • I4SubmodelElementCapability. Based on IDTA-01001-3-0, describes a generic RAMI4.0 SubmodelElement representing an Capability of a referenced Asset Administration Shell

  • I4SubmodelElementRelationship. Based on IDTA-01001-3-0, describes a generic RAMI4.0 SubmodelElement representing an Relationship of a referenced Asset Administration Shell

Two new data models ProcessChemicalAnalysis, ProcessEvent at a new subject dataModel.IndustrialProcess

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There are two new data models, ProcessChemicalAnalysis and ProcessEvent in the subject dataModel.IndustrialProcess

Thanks to the project ALCHIMIA and to the entities BFI (VDEh-Betriebsforschungsinstitut) and Scuola Superiore Sant’Anna the contributors,

Coal to chemicals routes diagram

soon there will be more models

updated the test file test_duplicated_attributes

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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

New pysmartdatamodels Version 0.8.0.2: Enhanced with More Models and Automated Contributor Tools

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We are thrilled to announce a significant new release of our Python package, pysmartdatamodels, designed to empower developers and streamline the contribution process for our community.

This update is packed with new data models and, most notably, powerful new validation and testing services.

What’s New in This Version?

An Expanded Library of Data Models

The heart of the Smart Data Models initiative is our comprehensive library of open-licensed data models. With this new release, we have expanded it further, adding new models across our thirteen domains, from Smart Cities to Smart Energy. This package continues to provide powerful functions to integrate more than 1,000 standardized data models into your projects, digital twins, and data spaces.

New! Automated Testing for Contributors

To improve the quality and speed of contributions, we are excited to launch a brand new service for our contributors. You can now automatically test your data models before submitting them. This automated validation ensures that new models comply with our standards, making the review and integration process smoother for everyone.

For those who want to integrate this validation into their own workflows, the source code for the testing tool is also available.

Online Validation for Examples

In addition to the new contributor tool, you can also use the online service to validate payloads against existing data model examples.

Get the Latest Version

You can find all the details, explore the functions, and get the latest package from the Python Package Index (PyPI). The updated README file includes comprehensive documentation on all the new features.

➡️ Get the package on PyPI

Our Commitment to Open and Interoperable Data

We are committed to making data interoperability easier for everyone. These updates are a direct result of community feedback and the hard work of our contributors. A huge thank you to everyone who has helped make this possible!

Two new data models, PublicationAgent and VocabularyService in the subject Data Space

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There are two new data models, PublicationAgent and VocabularyService in the subject Data Space.

It is create as a derivative work of the DSSC create by members of the DSBA

Thanks to the contributor Jason Fox

  • ParticipantAgent. A data model for a Participant Agent (Connector) tool. This tool can implement multiple services like Credential Store, Contract Negotiation, Transfer Process, and Data Plane. It extends the base ToolInformation model.

  • VocabularyService. A data model for a Vocabulary Service tool. This service manages and hosts data models like vocabularies, ontologies, and schemas. It extends the base ToolInformation model.

 

3 new data models for device subject

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There are 3 new data models in the subject device thanks to the contribution of the project P2CODE.

Sense HAT – Raspberry Pi

  • PolarH10. A Data Model of Polar H10 Heart Rate Sensor with RR, HRV, HR, and ECG
  • SenseHat. Data model for Sense Hat sensor readings for an array of sensing capabilities for Raspberry Pi.
  • UWBAnchor. Data model for the Ultra Wideband (UWB) Anchor which are electronic devices that detect UWB pulses emitted by UWB Tags and forward them to the location server for calculating tag positions.

Thanks to the new contributors

Use smart data models as a service. First draft

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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

 

Smart Data Models API and Demo

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

API Service (pysdm_api3.py)

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

Key Features

  • 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

Endpoints

Endpoint Method Description
/validate-url GET Validate a JSON payload from a URL against Smart Data Models
/subjects GET List all available subjects
/datamodels/{subject_name} GET List data models for a subject
/datamodels/{subject_name}/{datamodel_name}/attributes GET Get attributes of a data model
/datamodels/{subject_name}/{datamodel_name}/example GET Get an example payload of a data model
/search/datamodels/{name_pattern}/{likelihood} GET Search for data models by approximate name
/datamodels/exact-match/{datamodel_name} GET Find a data model by exact name
/subjects/exact-match/{subject_name} GET Check if a subject exists by exact name
/datamodels/{datamodel_name}/contexts GET Get @context(s) for a data model name

Validation Process

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

Demo Script (demo_script2.py)

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

Features

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

Usage

  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

Requirements

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

Installation

pip install fastapi uvicorn httpx pydantic jsonschema

Running the API

python pysdm_api3.py

Running the demo

python demo_script2.py

Configuration

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

License

Apache 2.0