Smart City Architecture for Data Ingestion and Analytics: Processes and Solutions

Abstract

Smart city architectures have to take into account a large number of requirements related to the large number of data, different sources, the need of reconciliating them in a unique model, the identification of relationships, and the enabling of data analytics processes. Ingested data, static and realtime, must be stored, aggregated and integrated to provide support for data analytics, dashboard, making decision, and thus for providing services for the city. This means: i) compatibility with multiple protocols; ii) handle open and private data; iii) work with IOT/sensors/internet of everything; iv) perform predictions, behavior analysis and develop decision support systems; v) use a set of dashboards to make a real-time monitoring of the city; vi) consider system's security aspects: robustness, scalability, modularity, interoperability, etc. This approach is determinant to: monitor the city status; connect the different events that occur in the smart city; provide support for public administrators, police department, civil protection, hospitals, etc., to put in action city/region strategies and guidelines and obviously directly to the citizens. In the paper, we focus on data ingestion and aggregation aspects, putting in evidence problems and solutions. The solution proposed has been developed and applied in the context of the Sii-Mobility national smart city project on mobility and transport integrated with services. Sii-Mobility is grounded on Km4City ontology and tools for smart city data aggregation and service production.