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hnzz001铁杆木虫 (正式写手)
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请帮忙查询是否EI或SCI检索，谢谢
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Key technologies of massive concurrent data processing in smart city based on cloud computing Yongqiang He & Xuyang Zhang |
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polymer1211
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hnzz001: 金币+50, ★★★★★最佳答案, 谢谢 2020-12-04 11:10:22
sunshan4379: LS-EPI+1, 感谢应助! 2020-12-08 20:23:17
感谢参与，应助指数 +1
hnzz001: 金币+50, ★★★★★最佳答案, 谢谢 2020-12-04 11:10:22
sunshan4379: LS-EPI+1, 感谢应助! 2020-12-08 20:23:17
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Key technologies of massive concurrent data processing in smart city based on cloud computing Accession number: 20201208313951 Articles not published yet, but available online Article in Press Authors: He, Yongqiang 1 , 2 ; Zhang, Xuyang 1 Author affiliations : 1 College of Computer, Henan University of Engineering, Zhengzhou, China 2 Henan IoT Engineering Research Center of Smart Building, Zhengzhou, China Corresponding author: He, Yongqiang (yqhe@haue.edu.cn) Source title: International Journal of Computers and Applications Abbreviated source title: Int J Comput Appl Issue date: 2020 Publication Year: 2020 Language: English ISSN: 1206212X E-ISSN: 19257074 CODEN: IJCAFW Document type: Article in Press Publisher: Taylor and Francis Ltd. Abstract: With the rapid development of information technology, the production and management of all industries are inseparable from the use of Internet technology, and it is even more so in the construction of smart cities. To build. Urban construction involves the integration and intelligent analysis of massive multi-source heterogeneous data, and requires a big data processing platform with high scalability and high performance. Based on the above background, the purpose of this article is to study the key technologies of computer-based smart city massive concurrent data processing. This paper starts with the previous research on frequent pattern mining and analyzes its advantages and disadvantages, and proposes the merging and improvement of the FP-Growth algorithm. Based on this, the algorithm is parallelized. It has improved significantly, and has also greatly improved the processing of large-scale data. Experimental results show that the average amount of data processed per hour reaches 27532375, and it has good stability. The throughput of the real-time streaming processing part is significantly increased with the horizontal expansion of the cluster (the number of nodes increases). Among them, after the number of nodes reached 5, its throughput per second reached about 3W. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. Number of references: 25 Main heading: Data mining Controlled terms: Cloud computing - Data handling - Smart city Uncontrolled terms: Data collection - FP-growth algorithm - Frequent pattern mining - Heterogeneous data - Horizontal expansion - Intelligent analysis - Internet technology - Real time streaming Classification code: 722.4 Digital Computers and Systems - 723.2 Data Processing and Image Processing Numerical data indexing: Power 3.00e+00W DOI: 10.1080/1206212X.2020.1743040 Funding Details: NumberAcronymSponsor18A520005-- Funding text: This work was supported by Key scientific research projects of Henan Province (No. 18A520005). Database: Compendex Compilation and indexing terms, © 2020 Elsevier Inc. |
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