# Clustering Smart City Services: Perceptions, Expectations, Responses
## 2 Literature Review  
The pace of evolution in the domain of technology is amazing. In the group of well-established and streamline technologies like databases and computer networks, new arrivals of emerging technologies like the internet of things, cloud computing, big data, or virtual and augmented reality, bring new promises and new expectations to computer scientists. In the same context, citizens and communities are eager to enjoy secure, reliable, trusted, high performing services that will solve problems and will deliver value. The overall understanding of the current evolution in ICT is that sophisticated information systems integrate the following features that can be integrated into smart cities solutions [1; 2; 3]:  
* Databases and Data Warehouses technologies: advanced services for distributed, large scale data repositories capable of managing both structured and unstructured data [7]. In the context of smart city services, Data warehouse technology is a core technology. Issues related to data protection, data privacy, and data regulation have emerged and required delicate treatment. * Content Management and Collaboration Platforms: The variety of data in structured and unstructured formats, as well as the variety of micro contents produced and consumed in different application contexts, such as social networks, e-commerce platforms and information or business portals, is challenging the design and provision of smart city services [2; 3; 7]. * Advanced Computer Networks technology: a wide range of new generation computer networks has arrived providing a variety of constitutional and integrative components for smart city services. 5 g networks, Internet of Things together with mobile networking and distributed sensor networks are only a few of the possibilities of our times [7; 8; 9; 10; 11]. In the same context also social networks, social media and innovative collaborative, community-driven technologies [9; 10]. * Big Data and Analytics technology: Decision making oriented and policy-making driven approaches enhance the discovery of hidden patterns over big data and offer unique visualization options to policy and makers. Especially within the context of smart city research, these approaches have gained critical share and the potential for social impact is great [7; 11; 12; 13; 14]. * Social Mining and Social Impact technology: The utilization of users' profiles in local, and global context permit unforeseen capabilities for opinions mining, social mining and sentiment analysis over content and micro contents [7; 15; 16; 17]. The utilization of these advanced computational techniques targets directly to the social component of smart city applications. With the increasing interest of businesses and the industry for personalized services and the recent updates on data privacy protection regulation measures set new limitations and barriers to a widespread taxonomy of social mining application for smart city services [18; 19; 20]. The evolution of information and communication technologies in the last decade resulted with the arrival of several highly promising technologies including Artificial Intelligence, Internet of Things, Cloud Computing, Cognitive Computing, Virtual, Mixed and Augmented Reality, Sensors and 5 Gnetworks, and many others. As a result, the design of urban applications and smart city systems becomes more complicated and sophisticated. At the same moment, there is a paradox that we must analyze further: while computer scientists and ICT experts claim for the value of these technologies to real users of smart cities applications, there are not many types of research that elaborate on the perceived value of sophisticated smart city services by real users. In our view, technology has a critical social purpose in the context of smart cities. It must be beneficial for the society and has to respond to key social problems. Technology must be transparent and should deal with critical ethical concerns about security and privacy.