Selection of Effective Methods of Big Data Analytical Processing in Information Systems of Smart Cities

The practice of using Big Data models is widespread implementing the procedures of information and technological support of processes occurring in urban resource social and communication networks. For Big Data, a list of characteristics is presented in the format 10V including volume, velocity, variety, validity, value, veracity, visibility, virtual, variability and valence. At the same time, the urgent task is to select the category of Big Data analytical processing tools for smart city needs. Further development of the Big Data concept leads to an expansion of the list of properties and a variety of tools for their analytical processing. It is suggested to use the expert estimation and hierarchy analysis methods proposed by T. Saati to select an effective Big Data analytical processing method for needs in a smart city. The procedures for this method are described below. The analysis was carried out among the following alternatives as managed machine learning, unmanaged machine learning, data mining, statistical analysis, data visualization. The obtained results show the highest efficiency of the method of managed machine learning. Furthermore, it should be noted that this method might be implemented in appropriate procedures considering the possible need to extend the sets of characteristics and their parameters. The proposed method allows to evaluate the advantages and disadvantages of the tools available on the market to work with big data in a situation when the city authorities decide on the need for appropriate investment.