How to Design DataModels - 8th May 2019 Corresponding webinar recording: https://youtu.be/T_1DpKf6C_c Understanding and applying Standard Data Models. Chapter: Core Difficulty: 3 Audience: Technical Domain Specific Presenter: José Manuel Cantera (Senior Standardization Expert, FIWARE Foundation)

1. Data Modelling with NGSI
José Manuel Cantera Fonseca – FIWARE Foundation
May 2019 (Aligned with ETSI ISG CIM)

2. Information Model
1

3. 2
Information Model
Attributes
• Name
• Type
• Value
Entity
• EntityId
• EntityType
1 n
“has”
Metadata
• Name
• Type
• Value1 n
“has”
2

4. 3
Information Model (as UML) – NGSI-LD

5. 4
Information Model - Highlights
 NGSI Entity  Physical or virtual object.
 It has (one) Entity Type.
 Uniquely identified by an Entity Id (URI, mandatory in NGSI-LD)
 Entity has zero or more attributes identified by a name
 Property --> Static or dynamic intrinsic characteristic of an Entity
 GeoProperty (geospatial context)
 Relationship  Association with a Linked entity (unidirectional)
 Properties have a value. An NGSI value can be
 single value (Number, String, boolean, DateTime).
 null is not allowed in NGSI-LD and not recommended.
 complex (Array, Structured Value)
 Relationships have an object
 A URI which points to another entity (target of the relationship). Target can be a collection.

6. 5
Information Model – Highlights (II)
 Cross-Domain, core properties for giving context to your information are
defined in a mandatory way, to be used by API operations (e.g. geo queries)
 location  Geospatial location, encoded as GeoJSON.
 observedAt  Observation timestamp, encoded as ISO8601. (timestamp in NGSIv2)
 createdAt  Creation timestamp (of entity, attribute). dateCreated in NGSIv2
 modifiedAt  Update timestamp (of entity, attribute). dateModified in NGSIv2
 unitCode  Units of measurement, encoded as mandated by UN/CEFACT.
 Recommended practice
 Use URIs to identify your entities. (Mandatory in NGSI-LD).
 A URN schema is provided off-the-shelf. It enables to know in advance what Entity Type
an Entity id refers to
 urn:ngsi-ld:<Entity_Type_Name>:<Entity_Identification_String>

7. Case of Study 1
Smart City
6

8. 7
Smart City Data Model
Source: ETSI
Specification

9. 8
Classical JSON Representation (a.k.a. NGSIv2)
{
"id": "urn:ngsi-ld:Vehicle:A4567",
"type": "Vehicle",
"brandName": {
"type": "Property",
"value": "Mercedes"
},
"isParked": {
"type": "Relationship",
"value": "urn:ngsi-ld:OffStreetParking:Downtown1",
"metadata": {
”timestamp": {
"value" : "2017-07-29T12:00:04Z",
"type" : "DateTime"
},
"providedBy": {
"type" : "Relationship",
"value" : "urn:ngsi-ld:Person:Bob"
}
}
}
}
{
"id": "urn:ngsi-ld:OffStreetParking:Downtown1",
"type": "OffStreetParking",
"availableSpotNumber": {
"type": "Property",
"value": 121,
"metadata" : {
”timestamp": {
"value" : "2017-07-29T12:00:04Z",
"type" : "DateTime"
},
"reliability": {
"type" : "Property",
"value" : 0.7
},
"providedBy": {
"type" : "Relationship",
"value" : "urn:ngsi-ld:Camera:C1"
}
}
},
"location": {
"type": "geo:json",
"value": {
"type": "Point",
"coordinates": [-8.5, 41.2]
}
}
}

10. 9
JSON-LD (RDF friendly) representation (a.k.a. NGSI-LD)
{
"id": "urn:ngsi-ld:Vehicle:A4567",
"type": "Vehicle",
"brandName": {
"type": "Property",
"value": "Mercedes"
},
"isParked": {
"type": "Relationship",
"object": "urn:ngsi-ld:OffStreetParking:Downtown1",
"observedAt": "2017-07-29T12:00:04Z",
"providedBy": {
"type": "Relationship",
"object": "urn:ngsi-ld:Person:Bob"
}
},
"@context": [
"https://uri.etsi.org/ngsi-ld/v1/",
"https://schema.lab.fiware.org/ld/context/"
]
}
{
"id": "urn:ngsi-ld:OffStreetParking:Downtown1",
"type": "OffStreetParking",
"availableSpotNumber": {
"type": "Property",
"value": 121,
"observedAt": "2017-07-29T12:05:02Z",
"reliability": {
"type": "Property",
"value": 0.7
},
"providedBy": {
"type": "Relationship",
"object": "urn:ngsi-ld:Camera:C1"
}
},
"location": {
"type": "GeoProperty",
"value": {
"type": "Point",
"coordinates": [-8.5, 41.2]
}
},
"@context": [
"https://uri.etsi.org/ngsi-ld/v1/",
"https://schema.lab.fiware.org/ld/context/"
]
}

11. 10
Simplified representation (keyValues)
{
"id": "urn:ngsi-ld:OffStreetParking:Downtown1",
"type": "OffStreetParking",
"name": "Downtown One",
"availableSpotNumber": 121,
"totalSpotNumber": 200,
"location": {
"type": "Point",
"coordinates": [-8.5, 41.2]
},
"@context": [
"https://uri.etsi.org/ngsi-ld/v1/",
"https://schema.lab.fiware.org/ld/context/"
]
}
Equivalent in NGSI-LD and NGSIv2

12. Case of Study 2
Smart Agrifood
11

13. 1
2
Data Model: Smart Agrifood

14. 13
Classical JSON Representation (a.k.a. NGSIv2)
{
"id": "urn:ngsi-ld:Tractor:A4567",
"type": ”Tractor",
"brandName": {
"type": "Property",
"value": ”John Deere"
},
"speed": {
type": "Property"
"value": 12,
"metadata": {
”timestamp": {
"value" : "2017-07-29T12:00:04Z",
"type" : "DateTime"
}
}
},
"isLabouring": {
"type": "Relationship",
"value": "urn:ngsi-ld:AgriParcel:A456",
"metadata": {
"startedAt": {
"value" : "2017-07-29T12:00:04Z",
"type" : "DateTime"
}
}
}
}
{
"id": "urn:ngsi-ld:AgriParcel:A456",
"type": ”AgriParcel",
"location": {
"type" : "geo:json" {
"value" : {
"type": "Polygon",
"coordinates": [
[
100,
0
],
[
101,
0
],
[
101,
1
],
[
100,
1
],
[
100,
0
]
] }
}
}

15. 14
JSON-LD (RDF friendly) representation (a.k.a. NGSI-LD)
{
"id": "urn:ngsi-ld:Tractor:A4567",
"type": ”Tractor",
"brandName": {
"type": "Property",
"value": "Mercedes"
},
"speed" : {
"type": "Property",
"value": 12,
"observedAt": "2017-07-29T12:01:32Z"
},
"isLabouring": {
"type": "Relationship",
"object": "urn:ngsi-ld:AgriParcel:A456",
”startedAt": {
"type": "Property",
"value": "2017-07-29T12:00:04Z"
}
},
"@context": [
"https://uri.etsi.org/ngsi-ld/v1/",
"https://schema.lab.fiware.org/ld/context/"
]
}
{
"id": "urn:ngsi-ld:AgriParcel:A456",
"type": "AgriParcel",
"location": {
"type" : "GeoProperty" {
"value" : {
"type": "Polygon",
"coordinates": [
[
100,
0
],
[
101,
0
],
[
101,
1
],
[
100,
1
],
[
100,
0
]
] }
},
"@context": [
"https://uri.etsi.org/ngsi-ld/v1/",
"https://schema.lab.fiware.org/ld/context/"
]
}

16. 15
Simplified representation (keyValues)
{
"id": "urn:ngsi-ld:Tractor:A4567",
"type": ”Tractor",
”brandName": ”John Deere",
”speed": 12,
”isLabouring": ”urn:ngsi-ld:AgriParcel:A456",
"@context": [
"https://uri.etsi.org/ngsi-ld/v1/",
"https://schema.lab.fiware.org/ld/context/"
]
}
Equivalent in NGSI-LD and NGSIv2

17. FIWARE Data Models
1
6

18. 17
FIWARE Data Models
 Landing page: https://schema.fiware.org
 Open source project that has developed multiple data models
 https://github.com/FIWARE/dataModels
 Mainly for the Smart City domain but also some for the Smart Agrifood domain
 Specifications are crafted using markdown + JSON Schema
 Example Weather observed data model
 https://github.com/Fiware/dataModels/blob/master/specs/Weather/WeatherObserved/doc/spec.md
 https://fiware.github.io/dataModels/specs/Weather/WeatherObserved/schema.json
 Data Models Guidelines (how to develop new Data Models)
 https://github.com/Fiware/dataModels/blob/master/specs/guidelines.md
 Pull Request – Review Lifecycle.

19. See also
1
8

20. 19
References
 NGSI-LD FAQ
 https://github.com/FIWARE/dataModels/blob/master/specs/ngsi-ld_faq.md
 NGSI-LD HowTo
 https://github.com/FIWARE/dataModels/blob/master/specs/ngsi-ld_howto.md
 ETSI NGSI-LD Specification (January 2019)
 https://www.etsi.org/deliver/etsi_gs/CIM/001_099/009/01.01.01_60/gs_CIM009v010101p.pdf
 https://docbox.etsi.org/isg/cim/open/Presentation_for_WoT_Plenary_Meeting_Lyon__TPAC_.pdf
 FIWARE NGSIv2 Specification
 http://fiware.github.io/specifications/ngsiv2/stable/

21. Thank you!
http://fiware.org
Follow @FIWARE on Twitter
José Manuel Cantera Fonseca
FIWARE Foundation
Josemanuel.cantera@fiware.org