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https://dx.doi.org/10.23670/ir...
Article . 2022
License: CC BY
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РАЗРАБОТКА МОДУЛЯ, РЕАЛИЗУЮЩЕГО МЕТОДЫ И АЛГОРИТМЫ ОБРАБОТКИ РАЗЛИЧНЫХ ТИПОВ ПОТОКА ДАННЫХ

DEVELOPMENT OF MODULE IMPLEMENTING METHODS AND ALGORITHMS FOR PROCESSING VARIOUS TYPES OF DATA FLOW

РАЗРАБОТКА МОДУЛЯ, РЕАЛИЗУЮЩЕГО МЕТОДЫ И АЛГОРИТМЫ ОБРАБОТКИ РАЗЛИЧНЫХ ТИПОВ ПОТОКА ДАННЫХ

Abstract

В настоящее время объем данных, генерируемых машинами и человеческими взаимодействиями, быстро растет, и технологии развиваются, пытаясь решить эту проблему. Хотя большие данные широко обсуждаются на теоретическом уровне, существует ряд трудностей при их обработке.Целью данной работы является разработка модуля, который позволит классифицировать поток данных и затем обрабатывать его, принимая во внимание определенные параметры, такие как: типы файлов в соответствии с расширением типа, дата, имя и размер файла, используя в доказательство определенные методы и алгоритмы.Очевидно, что этот модуль позволит легче и быстрее обрабатывать и устранять определенные трудности, связанные со структурой больших данных.

The amount of data generated by machines and human interactions is now growing rapidly, and technologies are evolving trying to solve this problem. Although big data is widely discussed on a theoretical level, there are a number of difficulties in its processing.The aim of this work is to develop a module that will classify a data flow and then process it, taking into account certain parameters, such as: file types according to type extension, date, file name and size, using certain methods and algorithms as proof.Obviously, this module will allow easier and faster processing as well eliminate certain difficulties associated with the structure of big data.

Международный научно-исследовательский журнал, Выпуск 9 (123) 2022

Keywords

набор данных, Big data, big data structures, algorithm, алгоритмы обработки данных, большие данные, data set, алгоритм, структуры больших данных

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green