Author: maestro

Training on Smart Data models

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Tomorrow at day 0 of the FIWARE summit there will be a training session about Smart Data Models.
Slides are freely shared here

This training will be delivered face to face.

  • Intro:
    • Board and what is Smart Data Models Program
    • Current Status
    • Contributos and dissemintions
  • Agile standardization
    • Current standardization status
    • M.A.S.: Manifesto for agile standardizationç
    • Standardization in a digital market
  • Data model contents
    • Contents of a data model
    • Schema: review
    • Additional documents: examples, contributors, adopters, notes*
    • Generated documents: model.yaml, examples, README, context
  • Data Model creation
    • Incubated repository.
    • From csv, json or open data portals
    • Tests
  • Data Model moderation
    • Data model moderation
  • Future
    • pysmartdatamodels
    • New services. Integration
    • New data models. Mapping of standardizations
    • Other services for the contributors

MAS: Manifesto for Agile Standardization

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The Manifesto for Agile Standardization (MAS) describes the 7 principles that we apply to the Smart Data Models program.

0. Don't just standardize, be agile and standardize
1. Do not reinvent the wheel
2. Normalize real cases
3. Be open
4. Don't be overly specific
5. Flat not Deep
6. Sustainability is key

If you want to read the agile standardization manifesto’s complete explanation a one-page document is located at the root of our data models repository.

 

Announcements widget. Just thanks

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Not all changes in the data models are reported with a blog post, Twitter dissemination o LinkedIn post. But if you notice there is a widget in the right part of the front page (scroll down till Announcements) where these tiny changes are reported (see the image or browse the front page).

Most of them are issues, suggestions or PR solved thanks to the collaboration of the community. They make the data models better for all.

We cannot say thanks individually to all of them but please feel our gratitude.

You can download all of the tiny annoncements from this simple text file.

Just thanks

3 new data models for environment (and mobility)

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There are 3 new data models coming from the collaboration with the GreenMov project.

They are located at the Environment subject.

  • NoisePollutionForecast. Noise Pollution forecast stores the expectation about noise pollution based on some input elements and the noise elements present.

  • TrafficEnvironmentImpact. Environmental Impact of traffic based on the vehicles traffic and their emission characteristics

  • TrafficEnvironmentImpactForecast. Environmental Impact of traffic based on the vehicles’ traffic expectations and their emission characteristics

Vista aérea de las colas de acceso a Gibraltar (9460862160) (5)

Connecting Open Data Soft open data with Smart Data Models for drafting new data models

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Many of the datasets published in open data portals are extensively used elsewhere. Well-maintained portals have managers that document the data structure and provide definitions of the types and contents of every field in these datasets. These are some of the requirements for the successful publication of a new Smart Data Model.

With this simple python script (and others to come) at the utils folder it can be drafted a JSON schema compliant with the Smart Data Models Program contribution manual. It is an early version (not everything is updated) but you can check it out.

 Parameters:
– base url of the ODS portal
– dataset_id of the dataset
Returns:
A draft json schema compliant with Smart Data Models Program,

some limitations: it does not translate descriptions (required in English)
some data types
It prints the schema and also returns (if possible a file named schema.json)

test it with this command
SDM_OpenDataSoft_schema_converter.py https://data.ameli.fr/ effectifs

 

New customization option for context

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The Smart Data Models Program does not define canonically and uniquely the terms used in the data models. There are many ontologies and vocabularies providing solutions to this issue.

For those users of linked data solutions, every subject includes a context.jsonld file (see example) with long IRI for the terms used in the data models. Besides this, the IRI provided are in fact URL to pages with additional information about the term (see example).

not only this but also two services are available on the tools menu on the front page.

  1. Merging several contexts and detecting conflicts. (for merging several context from different subjects)
  2. Mapping a context with external ontologies. (mapping a local context from SDM to any external ontology). These are the ones available but more could be easily created on demand.

But when mapping an existing open and adopted standard now it is possible to customize the context generated by using a new file notes_context.jsonld (see this empty example) at the root of the subject. It will replace the automatic IRI for a term with the customized one.