The secondary use of data concerns the use of data for purposes that are different from the purpose for which data was initially collected. Although data manage The secondary use of data concerns the use of data for purposes that are different from the purpose for which data was initially collected. Although data manage

Big data challenges different aspects of storing, processing and managing data, as well as analyzing and using data for business purposes. Applying Data Mining methods over Big Data is another challenge because of huge data volumes, variety of information, and the dynamic of the sources. Different applications are made in this area, but their successful usage depends on understanding many specific parameters. In this paper we present several opportunities for using Data Mining techniques provided by the analytical engine of RDBMS Oracle over data stored in Hadoop Distributed File System (HDFS). Some experimental results are given and they are discussed.

Data Mining accomplishes nontrivial extraction of implicit, previously unknown, and potentially useful information of data. The aim of data mining is to discover knowledge out of data and present it in a form that is easily comprehensible to humans. Neural Networks are analytic techniques capable of predicting new observations from other observations after executing a process of so-called learning from existing data. Neural Network techniques can also be used as a component of analyses designed to build explanatory models. Now there is neural network software that uses sophisticated algorithms directly contributing to the model building process. The latest developments in research on neural networks bring them much closer to the ideal of data mining: knowledge out of data in understandable terms. The main goal of the review is to compare neural networks with other techniques for data mining and to overview some examples of application of neural networks to data mining processes in practice.

Ensuring noise immunity is one of the main tasks of radio engineering and telecommunication. The main task of signal receiving comes down to the best recovery of useful information from a signal that is destructed during propagation and received together with interference. Currently, the interference and noise control comes to the fore. Modern elements and methods of processing, related to intelligent systems, strengthen the role of the verification and recognition of targets. This makes noise control particularly relevant. The most-important quantitative indicator that characterizes the quality of the useful signal is the signal-to-noise ratio. Therefore, determining the noise parameters is very important. In the present paper, a signal model is used in the form of an additive mixture of useful signals and Gaussian noise. It is an ordinary model of a received signal in radio engineering and communications. It is shown that the phase portrait of this signal has the shape of an ellipse at the low noise level. For the first time, an expression of the width of the ellipse line is obtained, which is determined by the noise dispersion. Currently, in electroencephalography, diagnosis is based on the Fourier transform. But, many brain diseases are not detected by this method. Therefore, the search and use of other methods of signal processing is relevant.

Data warehouses are a widespread tool for data processing. The article explores different points of view and expectations of the stakeholders of modern data war