Classification characteristic for heterogeneous data processing tasks

R.A. Bagutdinov

Abstract


The paper considers some aspects of solving the problem of fast, correct and efficient choice of data processing methods based on the classification characteristics of heterogeneous data and the corresponding specific criteria. Based on theoretical studies, including in the field of system analysis, a classification analysis of heterogeneous and different-scale data and related methods of their processing, including using mathematical statistics methods, was carried out. The author made an attempt to classify the main, most frequently encountered data processing methods for multisensory systems in order to identify recommendations for finding a more efficient and quicker solution to the problem that is necessary for the researcher. The relevance of this approach is supported by poorly formulated tasks and universal recommendations, depending on the degree of significance of the type of data for solving a particular practical problem.


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References


Aho Al'fred, Hopkroft D., Ul'man D. Struktury dannyh i algoritmy / Per. s angl.: M: Izdatel'skij dom «Vil'jams», 2003. 384 s.

Bagutdinov R.A., Zaharova A.A. The task adaptation method for determining the optical flow problem of interactive objects recognition in real time. Journal of Physics: Conference Series. 2017;803(1):012014.https://doi.org/10.1088/17426596/803/1/012014

Bagutdinov R.A. Gnoseologicheskie aspekty k opredeleniju naznachenija i sostava STZ v zadachah proektirovanija i razrabotki robototehnicheskih kompleksov. Programmnye sistemy i vychislitel'nye metody. 2017;1:39-45. https://doi.org/10.7256/2454-0714.2017.1.20372

Bagutdinov R.A., Nebaba S.G., Zaharova A.A. Algoritm obrabotki raznorodnyh dannyh dlja mul'tisensornoj STZ na primere analiza temperatury i koncentracii gaza / V sbornike: GRAFIKON-2017 Trudy 27-j Mezhdunarodnoj nauchnoj konferencii. 2017.. S. 97-100.

Ostrovskij O.A. Algoritmy provedenija osmotrov cifrovyh nositelej informacii dlja predotvrashhenija komp'juternyh prestuplenij. Voenno-juridicheskij zhurnal. 2017;11:3-6.

Ostrovskij O.A. Princip ob"ektnoj dekompozicii v sistematizacii identifikacionnyh kodov, harakterizujushhih prestuplenija v sfere komp'juternoj informacii. Policejskaja dejatel'nost', 2017;3(3):10–18. https://doi.org/10.7256/2454-0692.2017.3.21869

Galjamov A.F., Rizvanov D.A., Smetanina O.N., Jusupova N.I. Modeli i algoritmy global'no raspredeljonnoj obrabotki slabostrukturirovannyh dannyh na osnove mikrorazmetki dlja podderzhki prinjatija reshenij // Fundamental'nye issledovanija. – 2017. – # 1. – S. 27-35

Muhitova A.A., Zhizhimov O.L. Adaptivnye tehnologii pri postroenii administrativnyh graficheskih interfejsov dlja geterogennyh informacionnyh sistem dlja vvoda i redaktirovanija dannyh / V sbornike: Raspredelennye informacionnye i vychislitel'nye resursy. Nauka – cifrovoj jekonomike (DICR-2017) Trudy XVI vserossijskoj konferencii. Institut vychislitel'nyh tehnologij SO RAN. 2017. S. 142-149.

Sibirjakov M.A., Vasjaeva E.S. Modifikacija i modelirovanie algoritmov obrabotki dannyh v kjesh-pamjati sistem hranenija dannyh / Kibernetika i programmirovanie. — 2016. - # 4. - S.44-57. doi: 10.7256/2306-4196.2016.4.18058.

Jurevich E. I. Sensornye sistemy v robototehnike: ucheb. posobie / SPb.: Izd-vo Politehn. un-ta, 2013. -100 s

Hastie, T., Tibshirani, R., & Friedman, J. H. (2001). The elements of statistical learning : Data mining, inference, and prediction. New York: Springer.

Witten, I. H., & Frank, E. (2000). Data mining. New York: Morgan-Kaufmann.

Y. Maheo, F. Massi, N. Bouscharain, S. Milana, G. Le Jeune and Y. Berthier Degradation of high loaded oscillating bearings: Numerical analysis and comparison with experimental observations, Wear, vol. 317, pp. 141-152, Sep 2014.

Z. Pan, J. Polden, N. Larkin, S. Van Duin, and J. Norrish Recent progress on programming methods for industrial robots // Robotics and Computer-Integrated Manufacturing, vol. 28, pp. 87-94, 2012.

Zaytsev A. Variable fidelity regression using low fidelity function blackbox and sparsification // Lecture Notes in Computer Science,2016. V. 9653, P. 147–164.


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