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Table 2 Computational, analytical, technical, and logistic challenges.

From: On the sustainability of smart and smarter cities in the era of big data: an interdisciplinary and transdisciplinary literature review

Computational, analytical, technical, and logistic challenges
Design science and engineering constraints
Data processing and analysis
Data management in dynamic and volatile environments
Data sources and characteristics
Database integration across urban domains
Data sharing between city stakeholders
Data uncertainty and incompleteness
Data accuracy and veracity (quality)
Data protection and technical integration
Fault tolerance and scalability
Data governance
Urban growth and data growth
Cost and large-scale deployment
Evolving urban intelligence functions and related simulation models and optimization and prediction methods as part of exploring the notion of smart cities as innovation labs
Building and maintaining data-driven city operations centres or citywide instrumented system
Relating the urban infrastructure to its operational functioning and planning through control, automation, management, optimization, enhancement, and prediction
Creating technologies that ensure fairness, equity, inclusion, and participation
Balancing the efficiency of solutions and the quality of life against environmental and equity considerations
Privacy and security