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FIWARE Wednesday Webinars - How to Design DataModels

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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)

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Data Modelling with NGSI José Manuel Cantera Fonseca – FIWARE Foundation May 2019 (Aligned with ETSI ISG CIM)
Information Model 1
2 Information Model Attributes • Name • Type • Value Entity • EntityId • EntityType 1 n “has” Metadata • Name • Type • Value1 n “has” 2
3 Information Model (as UML) – NGSI-LD
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.
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>
Case of Study 1 Smart City 6
7 Smart City Data Model Source: ETSI Specification
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] } } }
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/" ] }
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
Case of Study 2 Smart Agrifood 11
1 2 Data Model: Smart Agrifood
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 ] ] } } }
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/" ] }
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
FIWARE Data Models 1 6
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.
See also 1 8
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/
Thank you! http://fiware.org Follow @FIWARE on Twitter José Manuel Cantera Fonseca FIWARE Foundation Josemanuel.cantera@fiware.org

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FIWARE Wednesday Webinars - How to Design DataModels

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