In the Smart City context, hundreds of data sets are available. Many of them are open data, accessible from local, regional, national, European public administrations, national institute of statistics, etc. These data can be static, statistical data or real time. In addition, many other data are produced by other institutions like Europeana, ECLAP, Getty, Voc, dbPedia, etc. Typically most of the data are geolocated and can be accessed as files in various formats (CSV, XLS, KMZ, JSON, XML, HTML, MySQL, ZIP, LSMA, SHP, etc.), other are accessible as Linked Data, Linked Open Data or via RDF Store end points (see dpPedia, Europeana, Senate of the Republic, Chamber of Deputies, ECLAP, km4city, etc.). Personal, private and critical data can be added to these open data. Some private data are produced by companies, like for example the position of car sharing vehicles, the position of taxis, busses, flows in the city, energy consumption data in a neighborhood, etc. Many of these data can be useful for public administrations to take decisions and to provide services. Personal data are related to a person, include personal identifications, the position of the person, its profile, etc., and need to be managed in accordance with terms of use and privacy policies. Finally, critical and personal data may be used by bad-intentioned to take actions against citizens security and infrastructures, and thus licensing and conditional access solutions are adopted. Data are typically produced by central data producers, and many of these can provide their data in different ways and formats. Among them: traffic management systems, fleet management, LTZ management, hospitals, weather, social network, etc. These data have to be accessible by an aggregator that makes queries, understanding and data integration. This is not a trivial operation since it implies the semantic understanding of the data that have to be uniformed in a single data model. A single and unified model of aggregated data allows making integrated queries to provide these data via API, and the possibility to realize services and applications. Examples of services could be those that allow geographical search, the production of suggestions based on statistical evaluations, geographic structure, similarities, etc., also on the basis of citizen behaviors on the city and with respect to the available services. Data aggregation and provision services enable the development of apps for tourism, cultural heritage, transport and mobility, personal services, wellness, energy saving, etc. actually these opportunities are difficult to be exploited for public administrations and companies. Mainly obstacles are related to the high costs of data integration and aggregation, due the limited interoperability among data that are produced in different periods by different entities and companies.

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