TitleTeaching The Big Scientific Data Analysis |
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AuthorsDr. Boris Sedunov, Russian New University, Russia |
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AbstractThe contemporary Human activity utilizes huge volumes of digital data to solve efficiently multiple social, economic, political, healthcare, environmental, scientific and technical problems. Now the big data analysis is mainly oriented to the socio-economic problems with a goal to lift the profit. The science and technology often limit the analysis area, concentrating, for example, only on properties of extra pure materials or isolated systems, to penetrate deeper in the nature of objects and systems under investigation. In the scientific analysis the initial data may be and should be regularized, thus diminishing the input data errors, and the analysis should be convergent. The big thermophysical data analysis appears as the most informative way to discover the properties of clusters in pure real gases, because the continuous spectrum of bound states in clusters prevents from the spectroscopic way for clusters' properties evaluation. The clusters now are considered as a new or still unknown state of mater. The goal of the paper is to teach the main principles of the regular scientific data convergent analysis basing on the author's experience to extract clusters' properties in pure real gases from regularized experimental thermophysical big data. The convergent analysis means: a mutual correspondence of raw scientific experimental data collected from all World; a correspondence of the regularized experimental data to universal polynomials; a correspondence of the processing mathematics to the physical nature of values; a correspondence of the individual models to the general physical picture of the Cluster World. |
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Keywordseducation, big data, regular scientific data analysis |
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CitationSedunov, B. (2021). Teaching The Big Scientific Data Analysis. In M. Shelley, W. Admiraal, & H. Akcay (Eds.), Proceedings of ICEMST 2021--International Conference on Education in Mathematics, Science and Technology (pp. 134-146). Monument, CO, USA: ISTES Organization. Retrieved 03 December 2024 from www.2021.icemst.com/proceedings/52/. |
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