Adaptive Integration Algorithm of Sports Event Network Marketing Data Based on Big Data

To address the issues of low-data integration accuracy and efficiency, as well as a lack of data integration impact, an adaptive data integration algorithm for sports event network marketing data based on big data is presented. The fundamental theory of tensor is researched by examining the notion a...

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Vydané v:Security and communication networks Ročník 2022; s. 1 - 9
Hlavní autori: Wu, Jiatong, Zhang, Jun, Qiao, Jing
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Hindawi 27.05.2022
John Wiley & Sons, Inc
Predmet:
Adaptive algorithms
Algorithms
Big Data
Data integration
Data processing
Decomposition
Digital marketing
Distributed processing
Efficiency
Global positioning systems
GPS
Internet of Things
Marketing
Mathematical analysis
Pattern recognition
Sports
Tensors
ISSN:1939-0114, 1939-0122
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Shrnutí:To address the issues of low-data integration accuracy and efficiency, as well as a lack of data integration impact, an adaptive data integration algorithm for sports event network marketing data based on big data is presented. The fundamental theory of tensor is researched by examining the notion and features of big data and by using the associated technologies of the big data framework. Collect network-marketing data from a variety of sporting events and feed it to a big data platform. Combined with MapReduce parallelization mode, tensor represents the online marketing data of sports events according to the structured, semistructured, and unstructured characteristics of different big data. Integrate each tensor model based on semitensor product, build a unified data adaptive integration tensor model, and realize the adaptive integration of sports event network marketing data. The experimental results show that the proposed algorithm has a good effect on the adaptive integration of sports event network marketing data and can effectively improve the accuracy and efficiency of data adaptive integration.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1939-0114
1939-0122
DOI:10.1155/2022/7660071