Page 44 - Redefining smart city platforms: Setting the stage for Minimal Interoperability Mechanisms - A U4SSC deliverable on city platforms

hybrid system. Several factors can drive the decision on where to store data. For instance, data
embedding sensitive information may call for on-premise systems in order to be compliant with
data protection and privacy regulations. On the other hand, whenever there are no restrictions on
the physical data storage location, and depending on the expected amount of data, cloud service
can be considered a flexible and viable solution. In this latter case, the system should take into
account the latency of the network. A hybrid between both solutions, where some data will be saved
in locally owned systems and some data in cloud services, would be also possible. From a usage
perspective, applications and services may require processing data in various formats. For instance,
structured data carries specific information that may fulfil the immediate needs of an application
or a service, whereas raw data can embed information that may be used in the near future. Thus,
the architecture should consider different data formats, providing storage support for unstructured
(e.g., raw data) and structured data and API to access historical data in a uniform manner. In order
to guarantee that data access is performed in accordance with their licence, policies of distribution
and/or charging, the system should support different data categories based on restriction on their
usage such as public or open data, private data and commercial data.

5.3.3 Data models

The adoption of standard and open data models facilitates the re-use of assets and solutions,
avoiding vendor lock-in. The system has to support open and standard data models and metadata
by providing pre-built taxonomies to describe assets (e.g., data, services, applications, devices), to
simplify the definition of the assets description and to allow re-use of existing data models.

5.3.4 Dynamic data exchange

The architecture includes mechanisms to manage terms and conditions for data exchange, whether
or not there is a monetization aspect. Many local governments see the benefit in having a local data
ecosystem, or even a concrete IoT data marketplace in which data can be exchanged among users.
The best-known examples are in the energy sector, where commodities like renewable energy are
traded based on operational data. IoT Data Marketplace providers can define different governance
policies which, for example, allow different political approaches on the conditions imposed on
data collected in public space. The system should thus support fine grain management in terms
of validation procedures to be followed. Cities and communities should be able to decide how
to regulate the access to their data – either by vetting registration requests from data providers
and data consumers, or by allowing an open access – how to be federated with other cities and
communities, what type of data should be accessible (e.g., personal data, anonymized data). Quality
of published resources and providers (e.g., in terms of documentation, availability, completeness
and reputation), as well as easy asset discovery, should be supported to facilitate better interaction
between consumers and providers. Ultimately, as cities and communities consider inhabitants’ trust
as a key success factor, the system should provide tools that foster transparency on data usage and
sharing by tracking SLA agreements and by providing tools for auditing.

34 Redefining smart city platforms: Setting the stage for Minimal Interoperability Mechanisms