Abstract
Smart cities have been proposed as information technology strategies to generate solutions for the benefit of large cities to improve their quality of life, through phenomena identification tools that use artificial intelligence. Some work has been aimed at developing the infrastructure for monitoring events and the Internet of things, others merely on data analytics without an application system context. This work cites various investigations on data science processes of the smart cities and reports some of its works whose main topics are planning for the start of a smart city, the framework for the analysis of smart cities, and smart cities big data algorithms for sensors location. In these cases, the experiences in these cases are described as well as the trend towards a new process with the form of monitoring-analysis-evaluation-found pattern-driving object-decision-making and the future of smart cities is finally discussed.
TopBackground
In the first part of this section, Smart City is studied as a concept and as a paradigm as well as its dimensions and areas of extension in the city. In the second part, the trajectories followed by its investigation and implementations are presented, envisioning the main challenge that is not to exclude the humans from this process. In the third part, Data Mining is analyzed and compared with Data Science, and Data Science with Big Data Analytics alluding to technologies and Machine Learning, optimization or bio inspired methods immersed in them. It also describes the importance of communication of analysis results, ethical and social aspects that contract errors in scientific models. The fourth part mentions the Smart Cities processes immersed in apps classified by the problems that these solutions attack with their respective Data Science process.
Key Terms in this Chapter
Apps Smart City: Applications that resolve problems of services and lack of resources in smart cities through data analytics.
Machine Learning: Supervised and unsupervised learning methods and techniques.
Intelligent Dispatch: Protocol and processes for the automatic sending of requests and attention to massive services in smart cities.
Data-Driven Visualization: Visualization techniques for the display of the results after the analysis emphasizing the ease of decision-making through the graphical interface.
Internet of Things: Hardware and software infrastructure with a network approach that uses sensors to capture data by monitoring events.
Big Data: Set of analytical techniques and methods for large volumes of data with the final obtaining of computing solutions.
Data Analytics: A process that is conformed by method and statistics techniques, computation, data mining, and big data as well as the sequence of steps to follow that culminate with the visualization of patterns to the solution to a problem.
Open Data: Philosophy and web services for computer analytics that provide free access to data sets with content from different fields such as economy, environment, education, among others.
Smart People: Factors and technologies that contribute to the development of education, culture, human values, inclusion for citizens.