| Title | MIKADO: a smart city KPIs assessment modeling framework |
|---|---|
| ID_Doc | 37783 |
| Authors | De Sanctis, M; Iovino, L; Rossi, MT; Wimmer, M |
| Title | MIKADO: a smart city KPIs assessment modeling framework |
| Year | 2022 |
| Published | Software And Systems Modeling, 21.0, 1 |
| 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. |
| https://link.springer.com/content/pdf/10.1007/s10270-021-00907-9.pdf |
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