MIM2: Representing Data
OASC MIM2: Representing Data
Introduction
This MIMs allows cities and communities to overcome the challenges of working with data that are represented in different ways by providing recommendations for the use of consistent and machine-readable representations of data so that data from various sources can be efficiently used with confidence across the organisation and shared with collaborators as part of a local data ecosystem.
Objectives
To support cities and communities to use consistent and machine-undertstandable definitions of all the entities about which data is being captured in a data ecosystem. This includes syntactic and semantic definitions. This MIM supports data users to use data models, serialisations and formats that support specific use cases, in a minimal way.
Capabilities and Requirements
C1: All entities included in data sources are described using consistent data models to enable interoperability for applications and systems.
R1. Data models used for all entities in any data source shall be made explicit:
They shall be well documented and have descriptive metadata, where the terms used shall be semantically unambiguous.
For attributes related to units of measurement, time formats etc, the units, formats etc, used shall be made explicit. The data models should be catalogued* so that they can be easily findable.
R2a. Data models used shall be based (wherever possible) on commonly recognised standardised data models as listed in the section on Specifications below.
R2b. Where it is not possible to use existing standardised data models, efforts shall be made to extend existing standardised data models that are most closely aligned or to define new ones, following best practice and conventions of the community or organisation defining the data models.
R3. Data models used shall support the exchange of data by using the relevant requirements of the MIMs on Accessing data and Interlinking data
*Note: Should the cataloguing be done in a human readable way, the data models themselves would not necessarily need to be made explicit in a human readable way.
C2: Different data models for the same entity that are used within a common data sharing ecosystem should be easily transformable into a common data model
R1. A common data model for each key entity should be developed for any data ecosystem. That common data model should contain all the fields included within the different data models for that entity that are used within that data ecosystem. Each different data model used within that data ecosystem could then be transformed into the common data model.
*Note: As far as possible this common data model should be a standard data model for that entity and one that would also enable additional new fields to be included in the future
*Note: transformation from and to data models might potentially be done using generative AI. This is to be covered by the Mechanisms.
Specifications
The following list provides the recommended standardised sets of data models for MIM2. This will continue to be added to, as new and suitable sets are identified.
ISO/IEC JTC1 is developing the ISO/IEC 5087 series of standards on City Data Model, of which Part 1: Foundation level concepts (ISO/IEC 5087- 1:2023) has already been published and Part 2: City level concepts (ISO/IEC DIS 5087-2) is at the final draft stage.
SAREF: Smart Appliances REFerence (SAREF) ontology specified by ETSI OneM2M committee with the extension of SAREF4Cities provides an ontology focused on smart cities.
OneM2M base ontology (that is compatible with SAREF). Additionally, OneM2M provides the means to instantiate ontologies as a means to provide semantic descriptions of the data exchanged (through the use of metadata).
Core vocabularies of former ISA2 (now Interoperable Europe) like Core Public Service Vocabulary Application Profile used as the basis for the Single Digital Gateway Regulation that touches local governments, Core Person, Core Organization etc.
DTDL is the Digital twin Definition Language developed by Microsoft. This language is based on top of json-ld and the existing Fiware data models are converted in this format.
For spatial (and spatio-temporal) observation data, the provisions of MIM-7 (Places) about data encoding have to be taken into consideration.
For the mobility domain it is recommended to use DateX II.
For general use cases, where a more flexible, data model is needed, NGSI- LD compliant data models have been defined by organisations and projects, including OASC, FIWARE, GSMA and the SynchroniCity project and there is an ongoing joint activity of TM Forum and FIWARE to specify more under the Smart Data Models initiative, see https://smartdatamodels.org/
Last updated