Big Data Analytics for Smart Urban Systems

Saeid Pourroostaei Ardakani, Ali Cheshmehzangi

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Springer Nature Singapore img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / Geowissenschaften

Beschreibung

Big Data Analytics for Smart Urban Systems aims to introduce Big data solutions for urban sustainability smart applications, particularly for smart urban systems. It focuses on intelligent big data which takes the benefits of machine learning to analyse large and rapidly changing datasets in smart urban systems. The state-of-the-art Big data analytics applications are presented and discussed to highlight the feasibility of big data and machine learning solutions to enhance smart urban systems, smart operations, urban management, and urban governance. The key benefits of this book are, (1) to introduce the principles of machine learning-enabled big data analysis in smart urban systems, (2) to present the state-of-the-art data analysis solutions in smart management and operations, and (3) to understand the principles of big data analytics for smart cities and communities. 

Endorsements

‘Over the many years of collaboration between academia and industry, we noticed the common language is ‘big data’; with that, we have developed novel ideas to bridge the gaps and help promote innovation, technologies, and science’.
- Tian Tang, Independent Researcher, China

 ‘Big Data Analytics is a fascinating research area, particularly for cities and city transformations. This book is valuable to those who think vigorously and aim to act ahead’.
- Li Xie, Independent Researcher, China

‘For urban critiques, knowledge trains aspiring opportunities toward outstanding manifestations. Smartness has evolved or/ advanced rambunctious & embracing realities along (with) novel directions and nurturing integrated city knowledge’.
- Aaron Golden, SELECT Consultants, UK

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Schlagwörter

Urban Healthcare, Transportation Management, Machine Learning Techniques, Data Modeling, Big data analytics, Urban Sustainability, Smart Applications, Social Networks, Economy Planning, Data interpretation