| Title |
Framework to support the Data Science of smart city models for decision-making oriented to the efficient dispatch of service petitions |
| ID_Doc |
38609 |
| Authors |
Estrada, E; Maciel, R; Negrón, APP; López, GL; Larios, V; Ochoa, A |
| Title |
Framework to support the Data Science of smart city models for decision-making oriented to the efficient dispatch of service petitions |
| Year |
2020 |
| Published |
Iet Software, 14, 2 |
| DOI |
10.1049/iet-sen.2019.0044 |
| Abstract |
The evolution of Smart Cities conveys continuous changes involving a great number of variables, which might hamper the development of evaluation tools and methodologies. Most of the metric models for Smart City are based on the selection of key performance indicators (KPI) according to the specific model objectives. As different organisations propose their own indicators generating different models, it is difficult to get a straightforward comparison among models. With the aim of dealing with this and other disadvantages, in this study, a framework based on the application of Data Science to the KPIs is proposed. This framework represents an infrastructure that goes through the treatment of Open Data, facilitating the evaluation of different models comparison intended for decision-making, and to the final stage of dispatching service reports. There are four components that integrate this framework (i) a tree structure to manage the KPIs; (ii) a designed JavaScript Object Notation document for service dispatch; (iii) Web applications for evaluations based on Smart People with four scenarios and; (iv) the infrastructure for reception and attention of reports. |
| Author Keywords |
decision making; Java; public administration; Internet; smart cities; Data Science; smart city models; decision-making; service petitions; evaluation tools; metric models; key performance indicators; Open Data; service reports; service dispatch; Smart People; JavaScript Object Notation document; Web applications |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000526392100011 |
| WoS Category |
Computer Science, Software Engineering |
| Research Area |
Computer Science |
| PDF |
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/iet-sen.2019.0044
|