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1. Information Extraction from Multistructured Data and its Transformation into a Target Schema / Briukhov D.O., Stupnikov S.A., Kalinichenko L.A., Vovchenko A.E. // Selected Papers of the XVII International Conference on Data Analytics and Management in Data Intensive Domains. 2015.  pp. 81–90.
2. Bedarev N.V., Voinov A.A. Natural Language Texts and Methods of Structured Data Extraction // International scientific and technological conference of students and young scientists “Youth. The science. Technologies". 2018. No. 2. pp. 37-42.
3. Borisov A.V. Modern solutions and approaches to processing arrays of unstructured textual information in the field of big data // Problems of modern science and education. 2017.pp. 49-52.
4. Petrova I. Y., Goryanin S.V. Information and analytical system EcoHealth for storage and analysis of structured and unstructured big data // Engineering and construction bulletin of the Caspian Sea region: scientific and technical journal 2017. No. 3 (21). pp. 66–71.
5. Pogodin G.V., Figo D.M., Vasiliev E.N. Serialization of data structures for storage and transmission in information systems. Methods and means // "Youth in Science". Collection of reports of the 16th scientific and technical conference. 2017. No. 2. P. 231-236.
6. CSV URL: https://ru.wikipedia.org/wiki/CSV (date of the request: 11.12.2020).
7. Samoylenko N. Python for Network Engineers Release 3.0 URL: https://pyneng.readthedocs.io/_/downloads/ru/latest/pdf/ (date of the request: 12.12.2020).
8. Extensible Markup Language (XML) URL: https://www.w3.org/XML/ (date of the request: 12.12.2020).
9. Kanaev K.A., Faleeva E.V., Ponomarchuk Y.V. Comparative Analysis of Data Exchange Formats for Applications with Client-Server Architecture // Basic research. – 2015. – No. 2-25. – p. 5569-5572.
10. Pilgrim M. Dive into Python 3. 2010
11. Suchkova E.A., Nikolaeva Yu.V. Development of an optimal data storage structure for decision support systems // Cybernetics and programming. – 2016. No. 4. pp. 58-64.
12. Romanov A.C. Database model for storing texts and their characteristics // Reports of the Tomsk State University of Control Systems and Radioelectronics. 2008. No. 1. pp. 70-73.
13. Shevelev O.G. Representation of a set of texts in a relational database for the purpose of linguistic analysis. 2004.
14. Dovbenko A.V. Data storage in NoSQL systems on the example of MongoDB. 2015.
15. Koroteev M.V., Koroteev K. Review of Some Contemporary Trends in Machine Learning Technology // E-Management. 2018. pp. 26-35.
16. Ruycheva, A.P. Development of machine learning // Modern technologies in education: materials of an international scientific and practical conference. 2017. Part 1.pp. 232-237.
17. Mison: A Fast JSON Parser for Data Analytics / Li, Yinan, Katsipoulakis N., Chandramouli, B., Goldstein J., Kossman D. 2017.
18. Savin I.V. Analysis of data storage systems // Bulletin of the Tula State University Technical Sciences. 2019. pp. 193-196.
19. Basov O.O., Saitov I.A. Main channels of interpersonal communication and their projection onto infocommunication systems // Proceedings of SPIIRAS. (7), pp. 122–140.
20. The popularity of Python open-source systems is growing. Moscow: Open Systems, 2019.S. 5-11.
21. Langdale G., Lemire D. Parsing gigabytes of JSON per second // The VLDB Journal: The International Journal on Very Large Data Bases. 2019. 28(6). pp. 941.