Abstract
Smart decision making plays a central role for smart city governance. It exploits data analytics approaches applied to collected data, for supporting smart cities stakeholders in understanding and effectively managing a smart city. Smart governance is performed through the management of key performance indicators (KPIs), reflecting the degree of smartness and sustainability of smart cities. Even though KPIs are gaining relevance, e.g., at European level, the existing tools for their calculation are still limited. They mainly consist in dashboards and online spreadsheets that are rigid, thus making the KPIs evolution and customization a tedious and error-prone process. In this paper, we exploit model-driven engineering (MDE) techniques, through metamodel-based domain-specific languages (DSLs), to build a framework called MIKADO for the automatic assessment of KPIs over smart cities. In particular, the approach provides support for both: (i) domain experts, by the definition of a textual DSL for an intuitive KPIs modeling process and (ii) smart cities stakeholders, by the definition of graphical editors for smart cities modeling. Moreover, dynamic dashboards are generated to support an intuitive visualization and interpretation of the KPIs assessed by our KPIs evaluation engine. We provide evaluation results by showing a demonstration case as well as studying the scalability of the KPIs evaluation engine and the general usability of the approach with encouraging results. Moreover, the approach is open and extensible to further manage comparison among smart cities, simulations, and KPIs interrelations.
Highlights
Despite the diverse definitions of smart cities [1], they all target the achievement of a sustainable economic, societal, and environmental development, while enhancing the quality of living for their citizens
We argue that supporting a graphical visualization of key performance indicators (KPIs) evaluated over a given subject is quite relevant for the comprehensibility of the analysis especially for non-experts stakeholders and to ease the knowledge sharing among stakeholders
We focus on the Air Pollution (AP) KPI since it shows more complex calculations, whereas we leave the interpretation of the remaining simpler KPIs in the Appendix A as an exercise for the interested reader
Summary
Despite the diverse definitions of smart cities [1], they all target the achievement of a sustainable economic, societal, and environmental development, while enhancing the quality of living for their citizens. Smart cities are characterized by different dimensions (e.g., mobility, education, environment), each managed by different stakeholders (e.g., public administrations, private institutions) who not always communicate with each other. This makes it difficult for public administrations to have a complete overview of the city
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