The major companies are proposing solutions to make city smarter, focusing on specific set of domains, such as IBM [1, 2] on services for citizens, business, transport, communication, water and energy; [3] on governmental, educational, e-health, safety, energy, transport and utilities; CISCO on people, things and data [4], etc. Most of these solutions present a multi-tier architecture ranging from 3 to 6 layers [5]. On the other hand, the number of tiers is partially relevant to the transformation of data in value for business, and thus to services for the city users, and in opportunities for the enterprises and city operators interested in creating innovative and effective services, while exploiting city data and information [6–۸]. Also, the smart city ranking models are not suitable in putting in evidence these aspects, since they are mainly focused on counting the number of provided open datasets, smart services, solutions, or energy results [9,10]. As a general consideration, the main technical issues regarding smart city solutions are related to data access, aggregation, reasoning, access and delivering services via Smart City APIs. The final aim is serving city users in a smarter and more efficient manner, stimulating their participation to the city strategies. Therefore, collected and produced data are used to facilitate the creation of smart and effective services exploiting city data and information. Specific end-users’ smart services should be developed and managed by enterprises and city operators, rather than by the municipality. On the other hand, the municipality has to provide a flexible data access and services. This means to make effective and efficient the data access with their semantics, the service delivering, the access to define and control dashboards, and the interoperability with any other smart control systems active in the city (e.g., mobility, energy, telecommunication, fire brigade, security, etc.). In the world, municipalities/cities and public administrations are publishing huge amount of open data. These data can be coarsely aggregated for integration by using solutions such as CKAN [11], OpenDataSoft [12], ArcGIS and OpenData [13]. In most cases, these solutions for open data are suitable for collecting open data files and their indexing on the basis of corresponding descriptive metadata.