We have created a new subject, aligned with Asset Administration Shell, dataModel.AAS in the Smart Manufacturing domain.
The Corosect project is using NGSI-LD for using AAS assets coming from the document Specification of the Asset Administration Shell. Part 1: Metamodel.
Data models will be soon added.
Thanks to the contributors coming from these organizations
The new version does not provide new functionalities but an indication, including drafted code, about what is missing or in progress to the package can grow according to your needs.
The source code for the new version 0.7.0 is here at the data-models repository
There are 4 new functions drafted with the headings inputs and outputs and some recommendations for development.
1) validate_payload(datamodel, subject, payload)
2) create_QR_code(datamodel, subject)
3) include_local_datamodel(schema, subject, datamodel, contributors (optional), adopters (optional), notes(optional))
4) submit_datamodel(subject, datamodel, contributors (optional), adopters (optional), notes(optional), example_payload, notes_context, public_repository, credentials)
we will be glad to receive code or questions implementing this and we will include the authorship
There is a minor new version of the package pysmartdatamodels.
Now it allows to have in the metadata of a data model the direct links to the specification in the 8 languages.
you can access thanks to the function list_datamodel_metadata and accessing the objects with the keys, spec, spec_DE, spec_ES, spec_FR, spec_IT, spec_JA, spec_KO and spec_ZH
from pysmartdatamodels import pysmartdatamodels as sdm
subject = "dataModel.Weather"
dataModel ="WeatherForecast"
metadata = sdm.list_datamodel_metadata(dataModel, subject))
print(metadata["spec_ES"]
You can install the update
pip install –upgrade pysmartdatamodels
There is a new version of the python package pysmartdatamodels
to use it you have just to type
pip install pysmartdatamodels
in your system
Besides the update in the list of data models it includes two new functions
– look_for_data_model that allows approximate searches for a data model based on their name
– list_datamodel_metadata that returns the metadata of a data model including context link, data model version, model tags, link to the schema and to the yaml version of the schema, title, description, $id, required, links to the examples, link to the adopters, link to the contributors of the subject and a link to the sql export of a data model
These new functions are the result of some requests of data spaces managers and several others will be included soon to help you out in the management of the semantic contents of a data space.
A code example of these two new functions
from pysmartdatamodels import pysmartdatamodels as sdm
subject = "dataModel.Weather"
dataModel = "WeatherForecast"
# Look for a data model name
print("22 : ")
print(sdm.look_for_datamodel("WeatherFora", 84))
# retrieve the metadata, context, version, model tags, schema, yaml schema, title, description, $id, required, examples, adopters, contributors and sql export of a data model
print("23 : ")
print(sdm.list_datamodel_metadata(dataModel, subject))
Now in the tools menu (currently the last option) you have a complete list of the most relevant links to the assets of the data models compile in the Smart Data Models initiative.
You can check in the main menu -> Tools -> Metadata of the data models
The information is coded in a json format and includes:
| subject | |
| data model | |
| version of the data model |
|
| modelTags | |
| title | |
| url to $id of the schema |
|
| description of the data model |
|
| required attributes |
|
| yamlUrl link to the yaml version of the schema |
|
| jsonSchemaUrl link to the json schema (single-sourc-of-truth) |
|
| @context link to the context file |
|
| exampleKeyvaluesJson link to the NGSI v2 key values example (json) |
|
| exampleKeyvaluesJsonld link to the NGSI ld key values example (jsonld) |
|
| exampleNormalizedJson link to the NGSI v2 normalized example |
|
| exampleNormalizedJsonld link to the NGSI ld key values example |
|
| sql export in SQL of the attributes of the data model |
|
| adopters List of adopters of the data model |
|
| contributors List of contributors of the subject where is the data model |
Enjoy!
4 of the python scripts that are used to maintain the Smart Data models have been released.
Use them or study it at your own convenience. Glad to receive comments and improvements
Check them out here:
FIWARE Foundation has joined the “Metaverse Standards Forum” The Metaverse Standards Forum is a non-profit, member-funded consortium of standards-related organizations, companies, and institutions that are cooperating to foster interoperability for an open and inclusive metaverse.
The Smart Data Models initiative is willing to contribute to those standards with all its data models to enable its use in the metaverse.
We are thrilled to announce the latest update to pysmartdatamodels Python package, version 0.6.3, featuring a new function: generate_sql_schema()This addition enabling seamless generation of SQL schemas with just a few lines of code!
Introducing generate_sql_schemqa() Function:
With the new function, generate_sql_schemqa()Â pysmartdatamodels simplifies the process of creating SQL schemas for your data models by providing as input the model.yaml representation of a Smart Data Model.
The Korean translation of the specifications has been incorporated. Therefore every specification folder now it has a new file spec_KO.md with the Korean translation.
See an example:
Also the specification is linked from the README.md file at the front of every data model.
We provide a service to Generate a PostgreSQL schema SQL script from the model.yaml representation of a Smart Data Model.
You can access this service under this link following Tools > SQL service.
You need to provide as input the standard GitHub link to the model.yaml file or the raw version GitHub link.
Slack channel
Discord channel #smart-data-models
help on discord
Invitation to discord 
Validate a payload with a data model(external)