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Big data analytics for achieving smart city resilience Key factors for adoption

Zanabria, Victor LU and Mlokozi, Diana (2018) INFM10 20181
Department of Informatics
Abstract
The impressive growth of smart city initiatives can be widely witnessed worldwide. These cities are launched at the effort to improve quality of citizens’ life and help city planners optimize and operationalize management of urban infrastructures. Smart cities are characterized by technologies and systems that are sources of high-volume and high-variety data sources which can be gathered, stored and analyzed by big data analytics for development and implementation of resilient smart city programs. These programs are solutions to challenges susceptible to smart cities as a result of global pressures such as climate change and social mobility. Nonetheless, how to fully adopt this technology and exploit the value of big data analytics for... (More)
The impressive growth of smart city initiatives can be widely witnessed worldwide. These cities are launched at the effort to improve quality of citizens’ life and help city planners optimize and operationalize management of urban infrastructures. Smart cities are characterized by technologies and systems that are sources of high-volume and high-variety data sources which can be gathered, stored and analyzed by big data analytics for development and implementation of resilient smart city programs. These programs are solutions to challenges susceptible to smart cities as a result of global pressures such as climate change and social mobility. Nonetheless, how to fully adopt this technology and exploit the value of big data analytics for smart city resilience is still a key challenge facing most stakeholders. Since the adoption of new technologies in smart city implementing organizations turns out to be a complex venture and experience varies from city to city, this thesis aims to unveil the key factors for adoption of big data analytics for achieving smart city resilience. An understanding of the key factors for adoption is a key to a strategy to achieve smart cities resilience using big data analytics. (Less)
Please use this url to cite or link to this publication:
author
Zanabria, Victor LU and Mlokozi, Diana
supervisor
organization
course
INFM10 20181
year
type
H1 - Master's Degree (One Year)
subject
report number
INF18-007
language
English
id
8950489
date added to LUP
2018-06-19 13:32:27
date last changed
2018-06-19 13:32:27
@misc{8950489,
  abstract     = {{The impressive growth of smart city initiatives can be widely witnessed worldwide. These cities are launched at the effort to improve quality of citizens’ life and help city planners optimize and operationalize management of urban infrastructures. Smart cities are characterized by technologies and systems that are sources of high-volume and high-variety data sources which can be gathered, stored and analyzed by big data analytics for development and implementation of resilient smart city programs. These programs are solutions to challenges susceptible to smart cities as a result of global pressures such as climate change and social mobility. Nonetheless, how to fully adopt this technology and exploit the value of big data analytics for smart city resilience is still a key challenge facing most stakeholders. Since the adoption of new technologies in smart city implementing organizations turns out to be a complex venture and experience varies from city to city, this thesis aims to unveil the key factors for adoption of big data analytics for achieving smart city resilience. An understanding of the key factors for adoption is a key to a strategy to achieve smart cities resilience using big data analytics.}},
  author       = {{Zanabria, Victor and Mlokozi, Diana}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Big data analytics for achieving smart city resilience Key factors for adoption}},
  year         = {{2018}},
}