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Analysis and mathematical modeling of big data processing
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DOI：10.1007/s12083-020-00978-3.png)
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摘要
En 中文
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.
Keyword:
Big data
Modeling
Identification
Classification
Association rules
Data mining
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2.6
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2.2K
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2.9K
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