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The authors of the article, relying on various sources and archival documents, analyze the organization of missionary activities of religious associations of the Russian Orthodox Church in the North Caucasus during the colonization of the region. The main goal of this area of work was to restore the authority of the Orthodox Church among the Slavic population in the context of the spread of ideas of schism, as well as among the peoples of the region who previously professed Christianity. This activity was supposed to contribute to the incorporation of the region into the system of socio-cultural relations and strengthen the unity of the Russian Empire.

Theoretical foundations for artificial intelligence systems being applied in Big Data processing have been considered. Capabilities of the Hadoop framework and Deductor analytic platform have been analyzed and compared. A technology to mine data from news Web sites due to the developed Skraper program has been considered. There were discussed some issues, revealing capabilities of the Word2vec program that was used in scientific research and provided for the retrieved words being vectorized in a 300-dimension format.

The article represents theoretical foundations investigated for application of artificial intelligence systems in Big Data processing. The most comprehensive list of tools for data analysis and machine learning has been considered. A comparative Hadoop framework and Deductor analytical platform opportunity analysis has been performed.An AI-system has been proposed for predicting the cost of innovative products in the context of digitalization of the Russian economy. A hypothesis that a neural network makes it possible to obtain a forecast for the cost of innovative products in the Russian Federation has been put forward and proved. The neural network model included such parameters as GDP (billion rubles), key rate (%), RTS index, output of innovative products (billion rubles), costs of innovative products (billion rubles), dollar exchange rate (rubles), balanced profit (billion rubles), risk (σ), loans originated (billion rubles), VIX-Index and forecast for the volume of innovative products (billion rubles). The list of parameters presented reflects the development of both the economic sphere and Russia's financial sector quarterly for the period of from 2015 to 2018.Based on quantization and subsequent visualization of big data and using a multidimensional diagram, the artificial intelligence system developed allows revealing the GDP trend in Russia depending on the cost of innovative products and the VIX option stock-exchange quotation in the global economic landscape. The AI-system that enables prediction for the cost of innovative products using the "what-if" function in the Deductor platform has been developed.