In these links you can find the two presentations of the training camp held during past week FIWARE summit.
Category: Smart Robotics domain
New forms for joining SDM community
The last option of the menu, now include the forms to join the SDM community in the different roles:
Board Member: Organization managing the SDM together with the existing members FIAWRE, TMForum, IUDX and OASC
Contributor: People who bring new / update data models
Adopter: Organization or project using some of the data models
User: People willing to be informed about the updates on the Smart Data Models initiative
Smart Data Models Session at FIWARE summit
Next 12th of June 17:00 we will have a 1 hour session during the FIWARE summit in Vienna.
The session will be a group of short presentations (<12 min) about actual use cases using Smart Data Models.
Fill out the form if you are willing to participate as a speaker
Entities have their own pages
In the context.jsonld of every subject there are long IRI for every first-level attribute and for the names of the entities. See this example.
formerly the link of the attributes took you to an existing web page while the links of the entities take you nowhere. Strictly there is not need to take anywhere, however, it would be useful to create such pages with basic information. Now there are also links to the entities’ pages, where a new page is being created. See this example.
Now all of them have their own page with these sections
- Definition
- Version
- Original Schema
- Contributors of the Subject
- Adopters of the data model
- Examples of the data model
- Attributes of the entity
- Required Attributes
See here the example for the data model Anomaly.
MasterSheet for submission of simple data models
We have released a master sheet that simplifies the contribution to the smart data models.
It uploads the data model to a new repository called drafted.
The video help is also available
Steps for using the spreadsheet
0.- Make a local copy in your google account and grant permission the first time you use it (it warns you that it is not validated by google, true, but anyhow go to advance and grant the permissions
1.- Include your example in json key values
2.- Include the name of the data model, its title, the subject, and the description together with your email
3.,- Describe the attributes, their NGSI type, their data type, and the written description.
And click on the two buttons
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()
Slack channel
Validate a payload with a data model