Digitalization has enabled infrastructure and cities to be “smarter”; the use of physical space and energy, the transmission of information, the management of users, assets and processes, the operation of businesses and companies have been progressively digitalized. The main challenges of a Smart City is its definition, scope and interconnections; there are different approaches to Smart City implementations that vary from collaborative multidisciplinary environments, the addition of Information and Communications Technology (ICT) within its physical fabric to the use of Big Data for higher abstraction decisions. This paper presents the concept of Digital as a Service (DaaS), where any complete digitalization can be implemented independently of its associated physical infrastructure in a Cloud environment; DasS would enable an interoperable Virtual Digital Infrastructure (VDI). In addition, this paper reviews the current Digital Systems, Transmission Networks, Servers and Management Systems. The next Industrial Revolution will be founded on Artificial Intelligence that will entirely replace humans by taking production and management decisions based on the Internet of Things (IoT), the Cloud, BlockChain, Big Data, Virtual Reality and the combination of digital and real infrastructure or city. Digital as a Service would be its enabler by providing the entire interconnection, integration and virtualization of its Space, Services and Structure (3S).

As digitalization is gradually transforming reality into Big Data, Web search engines and recommender systems are fundamental user experience interfaces to make the generated Big Data within the Web as visible or invisible information to Web users. In addition to the challenge of crawling and indexing information within the enormous size and scale of the Internet, e-commerce customers and general Web users should not stay confident that the products suggested or results displayed are either complete or relevant to their search aspirations due to the commercial background of the search service. The economic priority of Web-related businesses requires a higher rank on Web snippets or product suggestions in order to receive additional customers. On the other hand, web search engine and recommender system revenue is obtained from advertisements and pay-per-click. The essential user experience is the self-assurance that the results provided are relevant and exhaustive. This survey paper presents a review of neural networks in Big Data and web search that covers web search engines, ranking algorithms, citation analysis and recommender systems. The use of artificial intelligence (AI) based on neural networks and deep learning in learning relevance and ranking is also analyzed, including its utilization in Big Data analysis and semantic applications. Finally, the random neural network is presented with its practical applications to reasoning approaches for knowledge extraction.