Now there is a new page statistics with the main statistics regarding the content of the site.
It can also be found from the More stats link in the widget on the upper right corner of the front page.
Those data models containing examples with a geoproperty, now there also include another file named: example-geojsonfeature.json with this format.
So not all the data models include this example, which is generated automatically (so there is not a need to be contributed).
See an example: https://github.com/smart-data-models/dataModel.PointOfInterest/blob/master/PointOfInterest/examples/example-geojsonfeature.json
Geoproperties were formerly not documented because it was not possible to include the description into the definition of the data model.
Previously, the definition of Geojson property was retrieved from the original source, which prevents the documentation to be added. No its has been cloned into the common-schema.json, and the description has been added. As a consequence, they appear in the specification of all data models (all of them have been updated) tonight.
Additionally, the schema checker (The test which assesses if a JSON schema is properly documented) no longer accepts any undocumented property (Example). So if you want a data model to be accepted then you need to get a 0.
The contribution manual has been adapted accordingly.
The #FIWARESmartFest. From 8-10 June, is a three days online event with >100 speakers from around the globe, and 35 hours “live on air” sessions of exciting deep-dives showcasing the power of #opensource, outstanding use cases, trending topics in tech, the FIWARE Accelerator DAY, networking opportunities on AirMeet, and much more.
On June 9th 18:00 CEST we will hold the Smart Data Models session with these speakers (alpha order):
- Alberto Abella (Data modeling Expert, FIWARE Foundation)
- Gert De Tant. Chief Technical Architect. OASC
- Iván Dvojak. (Marketing Director, Tourism Posadas, Province of de Misiones, Argentina)
- Pierre Gauthier. (Chief API Architect at TMForum)
- Antonello Monti. (Director ACS / Chair Automation of Complex Power Systems at RWTH University)
- Abhay Sharma. (VP Engineering, IUDX Program Unit, SID, IISc)
Have a look and don’t forget to grab yourself a free ticket. https://bit.ly/3bcRn0E
#opensource #opendata #datamodels #AI #blogchain #digitaltwins #smartcities #smartindustry #smartenergy #smartagrifoodindustry #smartwatermanagement #gaiax #smartdata #datasovereignty #dataspaces
There is a panel during the session and we expect that one or two questions could come from the attendants, or you can send it now at firstname.lastname@example.org
Coming soon the detailed contents of the session.
The dataModel.RiskManagement subject has available 10 data models for risk management.
- Asset. An item of value to stakeholders. An asset may be tangible (e.g., a physical item such as hardware, firmware, computing platform, network device, or another technology component) or intangible (e.g., humans, data, information, software, capability, function, service, trademark, copyright, patent, intellectual property, image, or reputation). The value of an asset is determined by stakeholders in consideration of loss concerns across the entire system life cycle. Such concerns include but are not limited to business or mission concerns.
- CyberAnalysis. The entity that represents analysis performed by digital tools to detect for example, network traffic anomalies
- Exposure. This entity contains a harmonized description of a generic Exposure Entity made for the Risk Assessment domain.
- GISData. This entity contains a harmonized description of generic GISData made for the Risk Assessment domain.
- Hazard. This entity contains a harmonized description of a generic Hazard entity made for the Risk Assessment domain.
- Measure. Specific measure translated into actions to be performed into the different systems
- Mitigation. The mitigation of consequences reduces the risk after an event has occurred. Therefore, this risk reduction measure is not suitable for the reduction of the likelihood of events but for the reduction of the negative consequences. Examples for consequence mitigation measures could be e.g. the construction of connection pipes to the neighbor water supplier(s) to get water from them in case of a breakdown of the own water supply, the construction of wells for an emergency supply or signing of contracts with organizations providing small mobile emergency water treatment plants.
- Risk. Effect of uncertainty on objectives. An effect is a deviation from the expected—positive and/or negative. Objectives can have different aspects (such as financial, health and safety, and environmental goals) and can apply at different levels (such as strategic, organization-wide, project, product and process). Risk is often characterized by reference to potential events and consequences, or a combination of these. Risk is often expressed in terms of a combination of the consequences of an event (including changes in circumstances) and the associated likelihood of occurrence. Uncertainty is the state, even partial, of deficiency of information related to, understanding or knowledge of, an event, its consequence, or likelihood.
- Vulnerability. This entity contains a harmonized description of a generic Vulnerability Entity made for the Risk Assessment domain.
Thanks to the Contributors
In the main menu option
Generate NGSI examples -> Geojson features format
This option allows you to generate random payloads compliant with a data model in Geojson features format.
The README of the different data models now contains a new option, to export the payloads as a geojson feature.
It is a new line at the README of every data model. It provides actual results when the data model has Geoproperties. Otherwise, a warning message is replied to the user.
Find here the minutes of the open session.
As a reminder, open sessions are really open. Every Monday 14:00 CEST. You just need to connect to this link https://bit.ly/smartdatamodels and if you want to present something edit this presentation and include your slides.
The open session is a 30.minutes meeting with 3 objectives:
1.- Show last developments. OPEN-SESSION-SMART-DATA-MODELS
2.- Answer questions regarding existing or coming data models
3.- Presenting to the community
There were comments about new data models to be contributed in the water sector and how to run a quick contribution.
There is a new subject into Cross Sector domain for mapping the DCAT-AP 2.0.1 standard into smart data models.
This is the standard for catalogs and other elements to be shared from open data portals.
See the specification of this standard.
The first data model is the CatalogueDCAT-AP which has been released.
This is just an advance of the session on the next 9th of June, 18:00 CET in the middle of FIWARE Fest.
FIWARE Fest is a three-day online event of world-class innovation, collaboration, and networking. See its agenda.
The smart data models session will include a keynote session and a panel of discussion about practical uses of the Smart Data Models.
You can register for this event here.