Nowadays, marketing research is increasingly important for the success of enterprises. Conducting marketing research reduces the risk of making wrong decisions in the analysis and development of marketing strategies, planning and control of marketing activities.
The existing development of the educational field at the intermediate stage of secondary and higher education is analyzed. The idea of creating a kind of intellectual recommendation system for creation of personal educational trajectories is proposed. Statistically, it is fair to assume that the choice of a future profession depends on different factors, but the basis is always grounded on at least an average level of success in the relevant subject areas.
It is necessary to determine the optimal methodology for the system of predictive analysis of equipment to prevent emergency situations. The system may include, in particular: data input/reading from sensors, processing/storage of information in a database using algorithms for processing Big Data and decision trees [1]. Identifying possible types of problems and making decisions on how to respond to them; training the system for more accurate response and decision-making.
Generalized structural model of Big Data information resource for e-commerce systems developed in this paper. The analysis and substantiation of possibility and expediency of use of the Big Data in the processes of e-commerce performed. Concept of metascheme for control of access to Big Data source developed. Application of such metascheme allows to form a subset of data resource relevant to a certain type of e-commerce tasks.
Big data and high performance computing are seen by many as important tools that will be used to advance science. However, the computational power needed for this promise to materialize far exceeds what is currently available. This paper argues that the von Neumann computational model, the only model in everyday use, has inherent weaknesses that will prevent computers from achieving the envisaged performance levels. First, these weaknesses are explored and the properties of a computational model are identified that would be required to overcome these weaknesses.
The platforms of the social media of the Internet, depending on their content have been analyzed in the paper. The classification that allows selecting groups by specific one's signs has been made. To rank the pages of users of virtual communities, it is suggested to use a modified PageRank algorithm. An approach based on the use of lexical analysis and algorithm for ranking and organizing data using the MapReduce paradigm is developed.
This article describes the features of classification methods and technologies, analytics Big data. Described group of methods and technologies, analytics Big data that are graded according to the functional relationships and formal model of information technology. The problem of the definition of ontology concepts analytics Big data.
The architecture and self-learning method of hybrid neuro-fuzzy systems for big fuzzy clustering in on-line mode are proposed in this paper. The architecture of proposed system represents the hybrid of the fuzzy general regression neural network and clustering self-organizing network. During a learning procedure in on-line mode, the proposed system tunes both its parameters and its architecture. For tuning of membership functions parameters of neuro-fuzzy system the method based on competitive learning is proposed.
The paper presents a brief description of engineering and scientific problems which arise at the steel plant PJSC “ArcelorMittal Kryvyi Rih” when organizing a repair workshop to fix industrial equipment.
The attention is paid to innovative methods of repair process based on intelligent agents and Industry 4.0 principles.
In this paper the contemporary technology to big data processing is analyzed. The software solution on Hadoop is developed. And the comparative results of the time efficiency in big data processing with Spark or Hive are described. The approaches to implement the software systems for big data processing with Spark or Hive are suggested.