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Peer-to-Peer Networking and Applications

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12-08-2020

Analysis and mathematical modeling of big data processing

Authors: Kairat Imanbayev, Bakhtgerey Sinchev, Saulet Sibanbayeva, Axulu Mukhanova, Assel Nurgulzhanovа, Nurgali Zaurbekov, Nurbike Zaurbekova, Natalya V. Korolyova, Lyazzat Baibolova

Published in: Peer-to-Peer Networking and Applications | Issue 5/2021

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Abstract

Big data processing is an urgent and unresolved challenge that originates from the intensive development of information technology. The recent techniques lose their effectiveness rapidly as the volumes of data increase. In this article, we will put down our vision of the basic approaches and models related to problem solving, based on processing large data volumes. This article introduces a two-stage decomposition of a problem, related to assessing management options. The first stage of our original approach implies a semantic analysis of textual information; the second stage is built around finding association rules in a database, processing them via mathematical statistics methods, and converting data and objectives to a vector. We suggest processing the collected news events by a semantic model, which describes their key features and interconnections between them in a specified subject area. The classification-based association rules allow assessing the likelihood of a particular event using a set chain of events. This approach can be applied through the analysis of online news in a specified market segment.

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Title: Analysis and mathematical modeling of big data processing
Authors: Kairat Imanbayev
Bakhtgerey Sinchev
Saulet Sibanbayeva
Axulu Mukhanova
Assel Nurgulzhanovа
Nurgali Zaurbekov
Nurbike Zaurbekova
Natalya V. Korolyova
Lyazzat Baibolova
Publication date: 12-08-2020
Publisher: Springer US
Published in: Peer-to-Peer Networking and Applications / Issue 5/2021
Print ISSN: 1936-6442
Electronic ISSN: 1936-6450
DOI: https://doi.org/10.1007/s12083-020-00978-3

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