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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| Vydáno v: | Security and communication networks Ročník 2022; s. 1 - 9 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Hindawi
27.05.2022
John Wiley & Sons, Inc |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Wu, Jiatong Qiao, Jing Zhang, Jun |
| Author_xml | – sequence: 1 givenname: Jiatong surname: Wu fullname: Wu, Jiatong organization: Department of International Commerce and BusinessKonkuk UniversitySeoul 05029Republic of Koreakonkuk.ac.kr – sequence: 2 givenname: Jun orcidid: 0000-0002-6531-8816 surname: Zhang fullname: Zhang, Jun organization: Department of International Commerce and BusinessKonkuk UniversitySeoul 05029Republic of Koreakonkuk.ac.kr – sequence: 3 givenname: Jing surname: Qiao fullname: Qiao, Jing organization: Physical Education CollegeHunan University of TechnologyZhuzhou 412007Chinahut.edu.cn |
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| Cites_doi | 10.1016/j.ins.2019.08.041 10.1155/2022/3398545 10.1016/j.comcom.2020.04.051 10.1016/j.knosys.2019.03.019 10.1007/s11227-019-02746-4 10.1002/nla.2288 10.4218/etrij.2018-0265 10.1007/s11227-020-03328-5 10.1109/TFUZZ.2019.2928518 10.1177/2158244020970940 10.1007/s00607-020-00830-9 10.1016/j.knosys.2020.105749 10.1016/j.ipm.2021.102656 |
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| Copyright | Copyright © 2022 Jiatong Wu et al. Copyright © 2022 Jiatong Wu et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| SubjectTerms | 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 |
| Title | Adaptive Integration Algorithm of Sports Event Network Marketing Data Based on Big Data |
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