A pattern based feasibility study of cloud computing for Smart Mobility solutions

Author(s):  
Michael Strasser ◽  
Sahin Albayrak
2017 ◽  
Author(s):  
So-Yeon Lee ◽  
Jong-Dae Kim ◽  
Hye-Jeong Song ◽  
Yu-Seop Kim ◽  
Chan-Young Park

Author(s):  
Kareem Nagy Areed ◽  
Mahmoud Badawy ◽  
Amira Haikal ◽  
Mostafa Elhosseini

The spread of omnipresent sensing technology brings with it an increasing number of innovative models. The smart mobility initiatives offer new opportunities for Intelligent Systems to maximize the utilization of real-time data that are streaming out of different sensory resources. In recent years, the convergence trend of Big Data, Cloud and IoT has received considerable attention in industry and academia. A huge amount of data is generated every day from information systems and modern digital technologies such as the Internet of things (IoT) and cloud computing. The analysis of these massive data requires a lot of effort at multiple levels to extract knowledge to facilitate decision-making. Big data analysis is therefore a topical area of research and development. The main objective of this survey is to propose Big Data, Cloud Computing, and IoT (BCI) Amalgamation Model. Additionally, this paper explores the big data characteristics, challenges, analysis techniques, and various tools associated with it. The recommendation of the suitable analysis techniques of big data that could reduce the time and increase efficiency is discussed.


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