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Academic Journal of Mathematical Sciences, 2023, 4(5); doi: 10.25236/AJMS.2023.040508.

Research on Big Data Acquisition Method Based on Mapreduce Algorithm

Author(s)

Jiaxi Guo, Chuansheng Wu

Corresponding Author:
Jiaxi Guo
Affiliation(s)

University of Science and Technology Liaoning, Anshan, China

Abstract

With the progress of science and technology, big data has become an indispensable part of our lives. The application fields of big data are very wide, from business decision-making, medical research to education, all of them are involved. In this paper, we will focus on the acquisition of big data, methods and algorithms. A big data analysis method, a system, a computer device, and a storage medium thereof, relate to the technical field of big data analysis. The method obtains the big data information accessed by the user during the target time period; and stores the big data information in a distributed database in slices according to time; then performs data analysis tasks based on predetermined rules on the big data information stored in the distributed database, and obtains the analysis results; configures a data caching server to cache the analysis results; and when obtaining a data query request from the front-end, the parameters of the request are cached in the data caching server according to the parameters of the data query request. When the front-end data query request is obtained, the corresponding analysis result data is called in the data cache server as the query result, and the query result is output to the front-end and visualized, which can reduce the burden of the processor of the big data analysis system, improve the access speed of the user, avoid the access lagging, and ensure the smoothness of the user's access.

Keywords

data analysis methods, systems, computer equipment, storage media

Cite This Paper

Jiaxi Guo, Chuansheng Wu. Research on Big Data Acquisition Method Based on Mapreduce Algorithm. Academic Journal of Mathematical Sciences (2023) Vol. 4, Issue 5: 54-61. https://doi.org/10.25236/AJMS.2023.040508.

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