updated common-schema.json

We have noticed an error in the description of the attribute location in the file common-schema.json, a file that is embedded in most of the data models. It does not affect the data types (so the validation of the schemas is not affected) but it affects the qualification of the NGSI type that it is not properly identified,

GeoProperty is written with capital middle P.

The file has been updated and soon it will be launched an update of all the data models affected.

pysmartdatamodels options in the tools menu

New options for pysmartdatamodels package. There are a new element in he tools menu to access the pysmartdatamodels package, its documentation, or a code example.

New service for customization of @context with official ontologies

Some of the data models available are generated according to some ontologies (like dataModels.OSLO). The @context provided for the subject includes IRIs with the smartdatamodels by default, however, the use of official ontologies can be required.

In order to allow this the file notes_context.jsonld was created and also included in the contribution manual (seection 1.5 slide 23).

Now the service for mapping the external ontologies (Home -> tools -> Mapper @context with external ontologies) can detect that instead of a regular configuration file a notes_context.jsonld is provided and applies the replacement of terms defined there.

It can be also used as an API call, in this link you have an example https://smartdatamodels.org/extra/create_external_referenced_context.php?localContext=https://raw.githubusercontent.com/smart-data-models/dataModel.OSLO/master/context.jsonld&configOntologies=https://raw.githubusercontent.com/smart-data-models/dataModel.OSLO/master/notes_context.jsonld

The structure of the notes_context.jsonld has to follow the template

  • {

“@context”: {

“Term1”: “uri_term_1”,

“Term2”: “uri_term_2”,

}

}

New version of pysmartdatamodels 0.5.41

There is a new version of the python package for smart data models  pysmartdatamodels 0.5.41

Changelog:

– The README include now the basic documentation of each function

  • New function to load in a dictionary all subjects and their data models, with their repository link, and related domains
  • New function to load in a dictionary all the attributes in every data model including  name of the property, data type, NGSI type, description, units, et

The data models available for python developers. pysmartdatamodels 0.5.40 published. Beta version.

Now you can find in pypi.org the python package pysmartdatamodels with 13 functions for the integrators of the data models (more than 800) in external systems and applications. It is a beta version. There is a function, update_data() that whenever is run, it updates the data models to the last version (including adding the new data models). The code is available at the utils directory.
This python package includes all the data models and several functions (listed below) to use in your developments.

If you want to be updated on this package you can join this mailing list (Announcements are sent only when something relevant happens). We love to get your feedback at info@smartdatamodels.org

There are several online tools to manage and to create the data models, generate examples or to adapt to existing ontologies. See the tools menu option at the home site.

Functions available include:

1- List all data models. Function list_all_datamodels()
2- List all subjects. Function list_all_subjects()
3- List the data models of a subject. Function datamodels_subject(subject)
4- List description of an attribute. Function description_attribute(subject, datamodel, attribute)
5- List data-type of an attribute. Function datatype_attribute(subject, datamodel, attribute)
6- Give reference model for an attribute. Function model_attribute(subject, datamodel, attribute)
7- Give reference units for an attribute. Function attributes_datamodel(subject, datamodel)
8- List the attributes of a data model. Function attributes_datamodel(subject, datamodel)
9- List the NGSI type (Property, Relationship or Geoproperty) of the attribute. Function ngsi_datatype_attribute(subject, datamodel, attribute)
10- Print a list of data models attributes separated by a separator. Function print_datamodel(subject, datamodel, separator, meta_attributes)
11- Returns the link to the repository of a subject. Function subject_repolink(subject)
12- Returns the links to the repositories of a data model name. Function datamodel_repolink(datamodel)
13- Update the official data model list or the database of attributes from the source. Function update_data()

Roadmap

1.- Create a proper documentation

2.- Function to allow submission of improvements (i.e. missing recommended units or model) and comments to the different data models. Currently, you can do it by searching for your data model here
https://smartdatamodels.org/index.php/list-of-data-models-3/ visiting the github repo and making your PR or raising your issues there.
3.- Function to submit a new data model to an incubation repository. Currently, this is done manually incubated repository. By filling this form you are granted to contribute with new data models.
4.- Include new functions like search for the subject of a data model or other that you can suggest to us at info@smartdatamodels.org

### some example code
from pysmartdatamodels import pysmartdatamodels as sdm
subject = "dataModel.Weather"
dataModel = "WeatherForecast"
attribute = "precipitation"
print(sdm.list_all_datamodels())
print(sdm.list_all_subjects())
print(sdm.datamodels_subject("dataModel.Weather"))
print(sdm.description_attribute(subject, dataModel, attribute))
print(sdm.datatype_attribute(subject, dataModel, attribute))
print(sdm.model_attribute(subject, dataModel, attribute))
print(sdm.units_attribute(subject, dataModel, attribute))
print(sdm.attributes_datamodel(subject, dataModel))
print(sdm.subject_repolink(subject))
print(sdm.datamodel_repolink(dataModel))
print(sdm.print_datamodel(subject, dataModel, ",", ["property", "type", "dataModel", "repoName", "description", "typeNGSI", "modelTags", "format", "units", "model"]))
sdm.update_data()

SQL export available for Postgresql

There is a new file ‘schema.sql‘ in all the directories of the data models. It is a SQL script for PostgreSQL.

The script creates the structure of a relational table containing the attributes defined in the data model. It also creates the data types for those attributes with an enumeration of values.

For those attributes being arrays or objects, it creates a JSON attribute (allowed in PostgreSQL).

If you need additional features in this export please report them to info@smartdatamodels.org.

See an example.

Postgresql elephant

Draft of a python package available

Now we have a draft version of a python package to integrate the smart data models with your developments. It is a beta version so you can expect some issues when using it. We will be glad if you report it at info@smartdatamodels.org or suggest new features. Thanks to Anthony Uphof for his contributions (only a few of them are in this draft, next version will include them)

To install

pip install -i test.pypi.org/simple/ pysmartdatamodels

The functions included are:

1- List all data models. Function list_all_datamodels()

2- List all subjects. Function list_all_subjects()

3- List the data models of a subject. Function datamodels_subject(subject)

4- List description of an attribute. Function description_attribute(subject, datamodel, attribute)

5- List data-type of an attribute. Function datatype_attribute(subject, datamodel, attribute)

6- Give reference model for an attribute. Function model_attribute(subject, datamodel, attribute)

7- Give reference units for an attribute. Function attributes_datamodel(subject, datamodel)

8- List the attributes of a data model. Function attributes_datamodel(subject, datamodel)

9- List the NGSI type (Property, Relationship or Geoproperty) of the attribute. Function ngsi_datatype_attribute(subject, datamodel, attribute)

11- Print a list of data models attributes separated by a separator. Function print_datamodel(subject, datamodel, separator, meta_attributes)

12- Update the official data model list or the database of attributes from the source. Function update_data()

New data model WaterQualityPredicted at WaterQuality subject

A new data model for water quality prediction is being released based on the actual use cases. Located at the water quality subject.

Thanks to the project https://www.digital-water.city/ for its contribution

  • WaterQualityPredicted. Water Quality Predicted data model is intended to represent predictions of water quality at a certain water mass (river, lake, sea, etc.) section

Thanks to the contributors.