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Fractal methods in intelligent technologies for processing large data streams

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dc.contributor.authorTuritsyn, M. I.
dc.contributor.authorMyshev, A. V.
dc.date.accessioned2024-11-21T08:22:30Z
dc.date.available2024-11-21T08:22:30Z
dc.date.issued2019
dc.description.abstract© 2019 Published under licence by IOP Publishing Ltd. In this work a new approach to the construction of models and logic circuits of algorithms and procedures for information technology processing and analysis of big data streams within the fractal paradigm is describes. In this case, data streams are defined as information objects whose physical nature can be arbitrary. The information object is investigated apart from any model or scheme, the logical scheme of intellectual technology is built in the form of: facts, regularities and reality. Fractal methods form the framework of the logical, algorithmic and content essence of the approach. The basic premise of the approach is as follows. First, the processing and analysis of data stream to determine whether it forms a fractal structure and construct a phase portrait of data stream as an information object. Second, to distinguish the areas of fractal percolation and aggregation in the multifractal structure of the stream, the phase portrait is used. Third, to estimate the spatial and temporal scales of fractal percolation and aggregation processes.
dc.identifier.citationTuritsyn, M. I. Fractal methods in intelligent technologies for processing large data streams / Turitsyn, M.I., Myshev, A.V. // Journal of Physics: Conference Series. - 2019. - 1189. - № 1. - 10.1088/1742-6596/1189/1/012045
dc.identifier.doi10.1088/1742-6596/1189/1/012045
dc.identifier.urihttps://www.doi.org/10.1088/1742-6596/1189/1/012045
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85065604386&origin=resultslist
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/17948
dc.relation.ispartofJournal of Physics: Conference Series
dc.titleFractal methods in intelligent technologies for processing large data streams
dc.typeConference Paper
dspace.entity.typePublication
oaire.citation.issue1
oaire.citation.volume1189
relation.isOrgUnitOfPublication4b108cd7-08a1-4350-ab3f-20450dec091d
relation.isOrgUnitOfPublication.latestForDiscovery4b108cd7-08a1-4350-ab3f-20450dec091d
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