Cities as Enablers of the Data Economy: Smart Data Models for Cities - 21 October 2020 Corresponding webinar recording: https://youtu.be/b0EWq5E5jAc Speaker: Alberto Abella (Data Modeling Expert and Technical Evangelist, FIWARE Foundation) Chapter: Smart Cities Difficulty: 2 Audience: Technical Domain Specific

1. Cities as Enablers of the Data Economy:
Smart Data Models for Cities
21-10-20
Alberto Abella
Data modelling expert
FIWARE Foundation

2. Introduction
1
Alberto Abella
Data modelling expert
FIWARE foundation

3. Learning goals
▪ Assuming the economic relevance of the cities and how to
develop this ecosystem for producing welfare for the citizens
▪ Understanding the need for standardization as the base for
reducing costs for technology adoption
▪ The Smart Data Models initiative and how it will help you on
your projects
▪ Understanding how to use smart data models and how to
contribute to them
2
● Citizen as consumer
● Citizen as user
● Citizen as creator
● Citizen as designer

4. Index
1. The city as a data ecosystem for creating economic impact.
2. Context / Digital Twin Data Management in FIWARE.
3. Relevance of standardization. New approach. Open principle.
4. The Smart data Models Initiative. Structure.
5. The Smart data Models Initiative. Examples.
6. The Smart data Models Initiative. Users and contributors.
3

5. 1. The city as a data ecosystem for creating economic
impact.
4

6. 5
FIWARE was created to ease solutions supporting the Smart Digital Life
building around open standards for managing Context / Digital Twin Data
that blurs the frontiers among application domains and enable the Data Economy
SMART CITIES

7. Digital Ecosystem
6
Smart city Digital Ecosystem. Source: Based on Abella, Ortiz-de-Urbina & De-Pablos (2015)

8. Economic impact of data economy
7
Size of the Smart cities market
Billions (pre Covid-19)
Avg yearly growth: 20%
Source: Mckinsey 2016

9. 2. Context / Digital Twin Data Management in FIWARE.
8

10. What are we referring to as Digital Twin?
▪ Digital Twin = Digital representation of an asset
• Characterized by attributes
□ Properties
□ Relationships Linked Data
• Values of attributes may change over time (or not)
• Typically have a location (but it is not a must requirement)
▪ (digital representation of) Context = Digital Twins Collection
▪ Basis for the development of any Smart Solution:
• Standard API for getting access to Digital Twin data (context)
• Common Data Models associated to Digital Twin classes
9

11. Modeling Context using Digital Twins
for Cities …
10
Entities
(Digital Twins)
Bus
• Location
• # passengers
• Driver
• License plate
Citizen
• Birthday
• Preferences
• Location
• ToDo list
Incident / claim
• Date
• Location
• Type
• Issuer
• Description
Shop
• Location
• Business name
• Franchise
• offerings
Attribute
Process / Analyze
/ Monitor
Digital Twin
representation
Context
(Real World)
capture actuate
update
notify /
query

12. Integration at multiple levels
11
Digital Twin
representation
Digital Twin
representation
Digital Twin
representation
Architecting
Smart Solutions
Integrating systems and
data within organizations
(system of systems)
Sharing Data across
organizations
3rd
systems
sensors
Smart Solution
System 3
System 4
System 1
System 2
Smart City
Smart Building
Smart
Logistics
Smart Grid

13. NGSI: a standard API for accessing Context / Digital Twin data
12
Process / Analyze
/ Monitor
Digital Twin
representation
Context
(Real World)
capture actuate
update
notify /
query
Application/Service
FIWARE NGSI API
(NGSIv2 → NGSI-LD)
Bus
• Location
• No. passengers
• Driver
• Licence plate
Citizen
• Name-Surname
• Birthday
• Preferences
• Location
• ToDo list
Shop
• Location
• Business name
• Franchise
• offerings
Context Broker

14. 3. Relevance of standardization. New approach. Open
principle.
13

15. NGSI API: Endorsement at global level
14
TM Forum supports FIWARE
NGSI for real-time access to
context information in cities
TM Forum and FIWARE
collaborate in development
of data marketplace platform
components
TM Forum and FIWARE also
collaborate in definition of
common smart data models
in collaboration with cities
ETSI created Jan 2017 an
Industry Specification Group
(ISG CIM) for defining a
Context Information
Management API
FIWARE NGSIv2 provided the
basis for the NGSI-LD specs
published by ETSI
FIWARE provides several
open source implementations
of ETSI NGSI-LD
The FIWARE Context Broker
Technology has been
selected as a new CEF
(Connecting Europe Facility)
Building Block
recommended to public and
private sector for
publication of right-time
context data
The European Data portal
will support the publication
of right-time Open Data
The GSMA has published a
Reference Architecture for
IoT Big Data Ecosystem
which recommends to
mobile operators
NGSI-LD plays the core role
in the defined Reference
Architecture

16. Approach to data models. ‘De facto’ standardization.
15
Concept Classic De-facto
Standardization body Stable and fully-dedicated entity → DIN Unstable or not fully dedicated → Fiware foundation
Standardization groups Extended and Balanced members from
relevant and interested actors
Interested users and developers with expertise on
the field
Consensus mechanisms Global Reviews by participants By contribution and Benevolent dictator
Prestige of standards By source entity By use of the results
Advantages Low-biased standards. Coherence with former
standardizations Predictable reviews
Funding based on standard costs and
members fees
Standard created together with implementation
Evolution by use
Quick reviews
Low cost
Disadvantages Creation of theoretical standards (never
implemented)
Slow reviews
Standardization hardly support costs
Potential biased standards
Low barriers to Potential competitors

17. 4. The Smart Data Models Initiative. Structure.
16

18. And what will happen to the data models you use when ….
Scenario:
Imagine you have to create data models for the development o integration of a service / application
What will happen to the data models you use when
▪ … your project ends.
▪ … you have to update it.
▪ … you were asked to make it interoperable with other/new initiatives?
▪ … you need to update it to new regulations / standards?
Wouldn’t be more efficient and useful to do it together?
17

19. ▪ A community site with detailed data models available for open use for multiple sectors
▪ Together with other relevant organizations in the curation of the different domains and subjects
▪ Providing coherence and consistency between data models across different domains
▪ To create a method for AGILE standardization and evolution these data models
▪ To provide extended usefulness to FIWARE platform users in terms of:
− Extended interoperability
− Reduced time dedicated to data model codying
− Accumulated experience tested in real case scenarios
− Mapped to be integrated with other platforms
▪ Using open licensing to allow extensive use and adoption
▪ Used in real case scenarios (and based on real use cases)
▪ Based on git platform and github as development frontend
▪ Consensus as main decision method
▪ Based on widely adopted standards (including ontologies and international schemas, i.e. schema.org)
Principles of Smart data models initiative
18

20. GITHUB
http://github.com/smart-data-models
- Oriented to developers
- All resources available
- Contribution by PR
- Issues on data models
SITE (Wordpress)
http://smartdatamodels.org
- Oriented to end users
- News on updates (subscription)
- Check attributes and enumerations
Structure: Webs
19

21. data-models
Umbrella repo
Cross
Sector
Smart
Manufacturing
Subject NSubject 1
Smart
Water
Smart
Robotics
Smart
Agrifood
Smart
Cities
Smart
Environment
Smart
Destinations
Smart
Sensoring
Subject 2 Subject 3 Subject 4
DOMAINS
REPOSITORIES
Readme
pointing to the
list of subjects
General info or
shared
resources
DATA-MODELS
- Guides for coding new data models
- Template for new data models and examples
- Directory for scripting tools to check data models
- Inventory of domains and data models
- Inventory of attributes and terms
- @Context for json-ld
SUBJECTS’ REPOSITORIES
Readme pointing to the list of data models for the objects
Contributors.md
subject-schema.json
DATA MODELS
README.md
/doc/spec.md
/examples
schema.json
Current Adopters
Structure: domains and subjects compile data models
20

22. - Global data about the initiative
○ List of data models
○ List of attributes
○ Required attributes
- Domains’ repositories
- Updated daily to the last commit of Subjects
- Free contribution repo
- Under request
Domain repositories and others. http://github.com/smart-data-models/specs
Structure: Github. Domains
21

23. - Subjects as submodules of the Domain
- Updated daily to last commit
- Data models in the subject
Domain repositories and others. https://github.com/smart-data-models/SmartCities
- Shared elements for all the
Data models in the subject
Structure: Github. Domains
22

24. - Agreement for release data models with open license
- Contact options
- Presentation on the governance of the initiative
- Tools & links for learning about data modelling
- Manual submission of data model (with help)
- Newslists on different
Domains
- News updates on the
data models
- Document for future contributors to data models
- List of data models
- Attributes database
- Coding instructions
Frontpage. https://smartdatamodels.org
Structure: WP
23

25. Subscriptions: http://smartdatamodels.org/index.php/subscriptions-page/
Frontpage
1 list per domain
1 mail a week (unless very important changes)
Structure: WP
24
News:
https://smartdatamodels.org/index.php/news/

26. 5. The Smart data Models Initiative. Examples.
25

27. 26
Examples of use

28. 27
Example 1. Look for an attribute for your data model
1. Look for a parameter into the attributes database (i.e. light)
2. Explore the different data models related
3. Review the specification

29. Attributes and enumerations database
▪ Front page of https://smartdatamodels.org
▪ Searchable in > 3000 terms
▪ Across all data model
▪ Immediate answer and link to the schema
28

30. 29
Example 2. Creating an entity on NGSI and other systems
1. Browse github till retrieve GTFS trip payload (json)
2. Open editor to run the queries into a NGSI engine
3. Get example csv (raw)
4. Convert into SQL
5. Create a SQL database into a DB editor
By -stk - Own work, CC0,
https://commons.wikimedia.org/w/index.php?curid=47287176
sudo docker pull mongo:3.6
sudo docker pull fiware/orion
sudo docker network create fiware_default
sudo docker run -d --name=mongo-db --network=fiware_default
--expose=27017 mongo:3.6 --bind_ip_all --smallfiles
sudo docker run -d --name fiware-orion -h orion --network=fiware_default -p
1026:1026 fiware/orion -dbhost mongo-db

31. 30
Example 3. Using example payloads of a data model
1. Just use this link (Weather observation)
2. Or this one (Device)
3. Proof of Concept to be extended

32. 6. The Smart data Models Initiative. Users and
contributors.
31

33. 32
Licensing Data Models
1. Preferred: Creative Commons 4.0
2. Apache 2.0 or other open licenses could be approved as long as they:
a. Recognise contributions
b. Allow free reuse of the data models
c. Do not impose other restrictions to use and adoption
3. Contributors has to fill a form to provide rights to the initiative for
releasing with those licenses (not losing their IPR)

34. 33
Management of contributions
CONTRIBUTORS
● Anybody can contribute as long as he/she meets guidelines (for checking automation)
● Participation can be as individual or representing an organization
● Contributors are explicitly recognised and vote relevant changes and contributions
● Contribution has to be based on real use of the data model
● Contribution manual explains:
a. Options for contribution (PR, issue, form)
b. Link to the guidelines for contribution
c. Documents needed for a complete data model (reduction coming)

35. 34
Roadmap

36. 1. Dramatic increase in the # of data models (See incubated repo)
− Adaptation of existing standards (Cities, Agrifood, Manufacturing, Energy, Water,
Robotics, Smart Destinations, etc)
2. Engagement of additional relevant organizations and bodies
3. Automation tools for reducing the workload for contributors
− Automatic creation of examples
− Generation of payloads
− Help on data model generation and testing
4. Survey to users to get actual needs
5. Growth the community relying on the decentralized governance
Roadmap
35

37. Q & A
36

38. Thank you!
http://fiware.org
Follow @FIWARE on Twitter
contact:
alberto.abella@fiware.org