Abstract
The use of new technologies and industrialization in everyday life have a direct impact on the consequences of cultural, scientific, and demographic change that diversifies and densifies social relations, and makes individuals more interdependent and complementary. The large amount of data (Big Data) derived from the use of New Technologies solutions to generate a knowledge emphasizes the emergence of new values born of Protestantism and which encourage individuals to search for perfection in economic and social activities. The main objective of this paper is to set up an Intelligent Observatory System on Information Technologies and Social Networks to Analyze, Predict, Explain and Better Understand Social Mutations. Therefore, the system that we propose in this paper puts forward social indicators through learning systems and data mining solutions, for qualitative measures and optimized intelligent calculus based on the behavior and interaction of individuals and/or and social groups. Allowing to take all the necessary measures to foresee the risks and to quantify the rate of the negative impact on the social mutation, and to understand the stakes of the social relations in a given society (For example: the passage from an ideology to a other, disappearance of cultures and appearance of others). This article discusses challenges and role of Big Data Analytics in Social Mutations sector through the use of the new technologies. Following a qualitative approach, this paper reveals the actions currently undertaken by the two categories in the Morocain societies in order to understand their internal and external behaviour.
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Ezznati, I., Cherrat, L., Ezziyyani, M. (2020). Intelligent Observatory System on Information Technologies, Social Networks and Infrared to Analyze and Predict Social Mutations. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2019). AI2SD 2019. Advances in Intelligent Systems and Computing, vol 1104. Springer, Cham. https://doi.org/10.1007/978-3-030-36671-1_35
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DOI: https://doi.org/10.1007/978-3-030-36671-1_35
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