Title: Open interactive education algorithm based on cloud computing and big data

Authors: Jing Wei; Lianguang Mo

Addresses: School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xu Zhou, 221116, China; College of Construction Management, Jiangsu Vocational Institute of Architectural Technology, Xu Zhou, 221116, China ' College of Management, Hunan City University, Yiyang Hunan, 413000, China

Abstract: In order to improve the self-learning ability of cloud computing and scheduling ability of big data resources in open interactive education, an open interactive education algorithm based on cloud computing and big data is proposed. An information flow model for open education big data is constructed, and big data mining is conducted to an open interactive education platform through the association rules mining method based on parallel scheduling to extract semantic ontology information feature quantity of interactive education; spatial attribute clustering is performed in the cloud computing environment according to the feature extraction results, and big data information is scheduled through the multi-feature weight allocation method. Simulation results show that in open interactive education, this method can cause relatively good output performance of big data mining, relatively high accuracy of feature information clustering of open interactive education and relatively strong feature resolution and recognition ability of data output, which meets the educational resource scheduling and allocation requirements of open education.

Keywords: cloud computing; big data; open education; data mining; parallel scheduling.

DOI: 10.1504/IJIPT.2020.107984

International Journal of Internet Protocol Technology, 2020 Vol.13 No.3, pp.151 - 157

Received: 17 Jan 2019
Accepted: 14 Apr 2019

Published online: 01 Jul 2020 *

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