Evaluating the Resilience of the Bottom-up Method used to Detect and Benchmark the Smartness of University Campuses

Carlo Giovannella, Diane Andone, Mihai Dascălu, Elvira Popescu, Matthias Rehm, Oscar Mealha

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

Abstract

A new method to perform a bottom-up extraction and benchmark of the perceived multilevel smartness of complex ecosystems has been recently described and applied to territories and learning ecosystems like university campuses and schools. In this paper we study the resilience of our method by comparing and integrating the data collected in several European Campuses during two different academic years, 2014-15 and 2015-16. The overall results are: a) a more adequate and robust definition of the orthogonal multidimensional space of representation of the smartness, and b) the definition of a procedure to identify data that exhibits a limited level of trust.
Original languageEnglish
Title of host publication 2016 IEEE International Smart Cities Conference (ISC2)
PublisherIEEE
Pages1-5
DOIs
Publication statusPublished - 1 Jan 2017
Externally publishedYes
EventIEEE International Smart Cities Conference (ISC2) - Trento, Italy
Duration: 12 Sep 201615 Sep 2016
https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7573871

Conference

ConferenceIEEE International Smart Cities Conference (ISC2)
CountryItaly
CityTrento
Period12/09/1615/09/16
Internet address

Fingerprint

ecosystem
learning
method
school

Keywords

  • smart city learning
  • learning ecosystems
  • smart city analytics
  • flow state
  • Maslow pyramid
  • Principal Component Analysis

Cite this

Giovannella, C., Andone, D., Dascălu, M., Popescu, E., Rehm, M., & Mealha, O. (2017). Evaluating the Resilience of the Bottom-up Method used to Detect and Benchmark the Smartness of University Campuses. In 2016 IEEE International Smart Cities Conference (ISC2) (pp. 1-5). IEEE. https://doi.org/10.1109/ISC2.2016.7580792
Giovannella, Carlo ; Andone, Diane ; Dascălu, Mihai ; Popescu, Elvira ; Rehm, Matthias ; Mealha, Oscar. / Evaluating the Resilience of the Bottom-up Method used to Detect and Benchmark the Smartness of University Campuses. 2016 IEEE International Smart Cities Conference (ISC2). IEEE, 2017. pp. 1-5
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abstract = "A new method to perform a bottom-up extraction and benchmark of the perceived multilevel smartness of complex ecosystems has been recently described and applied to territories and learning ecosystems like university campuses and schools. In this paper we study the resilience of our method by comparing and integrating the data collected in several European Campuses during two different academic years, 2014-15 and 2015-16. The overall results are: a) a more adequate and robust definition of the orthogonal multidimensional space of representation of the smartness, and b) the definition of a procedure to identify data that exhibits a limited level of trust.",
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Giovannella, C, Andone, D, Dascălu, M, Popescu, E, Rehm, M & Mealha, O 2017, Evaluating the Resilience of the Bottom-up Method used to Detect and Benchmark the Smartness of University Campuses. in 2016 IEEE International Smart Cities Conference (ISC2). IEEE, pp. 1-5, IEEE International Smart Cities Conference (ISC2), Trento, Italy, 12/09/16. https://doi.org/10.1109/ISC2.2016.7580792

Evaluating the Resilience of the Bottom-up Method used to Detect and Benchmark the Smartness of University Campuses. / Giovannella, Carlo; Andone, Diane; Dascălu, Mihai; Popescu, Elvira; Rehm, Matthias; Mealha, Oscar.

2016 IEEE International Smart Cities Conference (ISC2). IEEE, 2017. p. 1-5.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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Giovannella C, Andone D, Dascălu M, Popescu E, Rehm M, Mealha O. Evaluating the Resilience of the Bottom-up Method used to Detect and Benchmark the Smartness of University Campuses. In 2016 IEEE International Smart Cities Conference (ISC2). IEEE. 2017. p. 1-5 https://doi.org/10.1109/ISC2.2016.7580792