Category: Smart Water Domain

New service for customization of @context with official ontologies

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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

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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.

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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

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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

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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

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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.

 

New database of data models’ versions

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In the main menu, it has been extended the submenu of list data models with a complete database of all versions of the data models.

It includes not only the data model, subject, and version but also the data directly linked to the raw version of the data model.

You can find it at Home -> List of data models -> List of data models versions.

Semantic-versioning

Release source code for some of the services. For python developers.

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In the tools option of the main menu, there are a few services to help you to use, create and debug your data models.

Although working, some of these services are limited. We are releasing the code for some of the services to allow you to contribute with improvments.

It appears by the end of the page with texts like this:

“source code of this service if you want to improve it.”

Source code has an hyperlink pointing to the specific script in the utils directory.

They are created in python and connected to the WP forms in each option

Specifically

List of Adopters (use cases) and contributors available at Community menu

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On the front page under the Community menu, there are two new entries
– Adopters: Lists the use cases documented in the different data models. It includes searchable facilities. It contains these fields:  adopter, description, mail, organization, project, comments, start date, subject and data model.

If you want to be listed, just make a PR on the file ADOPTERS.yaml of the data model folder. (The PR has to include an example ‘payload’ on how you use it)

– Contributors: Lists the people. It contains these fields:  name, surname, mail, organization, project, comments, year, and subject.

If you want to be listed you have to have contributed to any of the data models of the subject and then make a PR on the CONTRIBUTORS.yaml at the root of the subject.

In both cases the attributes are not mandatory so they can be empty.