Abstract: The concept of smart city is widely favored, as it enhances the quality of urban citizens’ life, involving multiple disciplines. Consequent to the complex urban networks, data processing complexity has increased significantly. Thus, it creates a crucial demand to facilitate autonomous decision-making and real-time data processing and analysis of smart cities. Therefore, in this paper we propose a smart city framework based on Big Data analytics. The proposed framework operates in three levels 1) Data generation and acquisition level, 2) Data management and processing level, and 3) Application level. Moreover, we analyzed the water consumption, traffic congestion, and air pollution data of Surrey (Canada) and Aarhus (Denmark) cities to determine the threshold values for data filtering. The analysis shows that the proposed architecture offers useful insights to the community development authorities to improve the existing smart city architecture.

• Publication Date: 16-Dec-2016
• DOI: 10.15224/978-1-63248-113-9-11
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Abstract: This paper deals with automatic adaptation of e-learning courses. The adaptation tailors the course content in given situation to the student’s learning styles. This paper describes the Virtual Teacher that uses a set of rules to automatically adapt the teaching style. These rules compose of two parts: conditions on various students’ properties or learning situation; conclusions that specify different adaptation parameters. The rules can be used for general adaptation of each subject or they can be specific to some subject. The rule based system of Virtual Teacher is dedicated to be used in pedagogical experiments in adaptive e-learning and is therefore designed for users without education in computer science

• Publication Date: 16-Dec-2016
• DOI: 10.15224/978-1-63248-113-9-47
• Views: 0
• Downloads: 0