The concept of smart cities emerged as a strategy to mitigate the unprecedented challenges of continuous urbanization, increasing population density and at the same time provide better quality of life to the citizens and visitors [1]. A smart city is composed of smart components such as smart buildings, smart farms and smart hospitals, which constitute various city domains where the meaning of the label “smart” has different connotations in each domain [2]. ICT applications and intensive use of digital artifacts such as sensors, actuators and mobiles are essential means for realizing smartness in any of smart city domains [3]. However, “smartening” of various city domains is not enough for a city to be smart, whereas the interrelationship between the underlying city domains should be taken into account to realize city smartness [3, 4]. As such, a smart city is viewed as a whole body of systems or system of systems. This integrated view for a smart city implies cross-domain sharing of information [5]. This holistic view for smart city characterizes the meaning of “smart” in the context of “smart city” compared to smartening of particular city domain. On the other hand, the extensive use of digital technologies in various city domains and the diffusion of digital technologies in people’s daily life have boosted human-to-human, human-tomachine, and machine-to-machine interactions which yield massive volumes of data, commonly known as big data which is a mixture of complex data characterized by large and fast growing volumes datasets which go beyond the abilities of commonly known data management systems to accommodate. By analyzing these big data volumes, valuable insights and correlations can be extracted [6]. The process of analyzing big data to extract useful information and insights is usually referred to as big data analytics or big data value chain [6], which is considered as one of the key enabling technologies of smart cities [7, 8, 9].