Open AccessFramework to support the Data Science of smart city models for decision‐making oriented to the efficient dispatch of service petitions
Author(s) -
Estrada Elsa,
Maciel Rocío,
Negrón Adriana Peña Pérez,
López Graciela Lara,
Larios Víctor,
Ochoa Alberto
Publication year - 2020
Publication title -
iet software
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.305
H-Index - 43
eISSN - 1751-8814
pISSN - 1751-8806
DOI - 10.1049/iet-sen.2019.0044
Subject(s) - performance indicator , computer science , service (business) , javascript , metric (unit) , notation , process management , performance metric , key (lock) , data science , software engineering , engineering , world wide web , computer security , operations management , economy , management , arithmetic , mathematics , economics
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.