Framework to support the Data Science of smart city models for decision-making oriented to the efficient dispatch of service petitions
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...
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| Published in: | IET software Vol. 14; no. 2; pp. 159 - 164 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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The Institution of Engineering and Technology
01.04.2020
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| ISSN: | 1751-8806, 1751-8814, 1751-8814 |
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| 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. |
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| AbstractList | 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 | Negrón, Adriana Peña Pérez López, Graciela Lara Larios, Víctor Ochoa, Alberto Estrada, Elsa Maciel, Rocío |
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| Keywords | Java key performance indicators evaluation tools Web applications service dispatch Open Data Data Science smart city models service reports public administration metric models decision-making service petitions JavaScript Object Notation document decision making smart cities Smart People Internet |
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| SubjectTerms | Data Science decision making evaluation tools Internet Java JavaScript Object Notation document key performance indicators metric models Open Data public administration service dispatch service petitions service reports smart cities smart city models Smart People Special Issue: Software Engineering Applications to Solve Organisations Issues Web applications |
| Title | Framework to support the Data Science of smart city models for decision-making oriented to the efficient dispatch of service petitions |
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