Research on Mining of Applied Mathematics Educational Resources Based on Edge Computing and Data Stream Classification

Facing the massive data of higher education institutions, data mining technology is an intelligent information processing technology that can effectively discover knowledge from the massive data and can discover important information that people have previously ignored from the huge data information. This article is dedicated to the development of applied mathematics education resource mining technology based on edge computing and data stream classification. First of all, this article establishes a resource system architecture suitable for existing applied mathematics education through edge computing technology, which can effectively improve the efficiency of data mining. Secondly, the data stream classification algorithm is used for information extraction and classification integration of massive applied mathematical education data. This method provides potential and valuable information for decision-makers and education practitioners. Finally, the simulation and performance test of the system verify that it has the functions of mathematical information mining and data processing. This system will provide strong support for applied mathematics education reform.

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Evaluating the Four-Way Performance Trade-Off for Data Stream Classification in Edge Computing

IEEE Transactions on Network and Service Management ◽

10.1109/tnsm.2020.2983921 ◽

2020 ◽

Vol 17 (2) ◽

pp. 1013-1025 ◽

Cited By ~ 1

Author(s):

Jessica Fernandes Lopes ◽

Everton Jose Santana ◽

Victor G. Turrisi da Costa ◽

Bruno Bogaz Zarpelao ◽

Sylvio Barbon

Keyword(s):

Data Stream ◽

Edge Computing ◽

Trade Off ◽

Stream Classification ◽

Data Stream Classification

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Data Stream Classification Algorithms for Workload Orchestration in Vehicular Edge Computing: A Comparative Evaluation

International Journal of Fuzzy Logic and Intelligent Systems ◽

10.5391/ijfis.2021.21.2.101 ◽

2021 ◽

Vol 21 (2) ◽

pp. 101-122

Author(s):

Mutaz Al-Tarawneh

Keyword(s):

Comparative Evaluation ◽

Data Stream ◽

Edge Computing ◽

Classification Algorithms ◽

Stream Classification ◽

Data Stream Classification

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Data stream classification Detection System Using Genetic Algorithm

i-manager’s Journal on Software Engineering ◽

10.26634/jse.6.1.1537 ◽

2011 ◽

Vol 6 (1) ◽

pp. 36-44

Author(s):

S. Jeya ◽

◽

S. Muthu Perumal Pillai ◽

Keyword(s):

Genetic Algorithm ◽

Data Stream ◽

Detection System ◽

Stream Classification ◽

Data Stream Classification

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Proceedings of the 14th Engineering Mathematics and Applications Conference

ANZIAM Journal ◽

10.21914/anziamj.v61i0.15015 ◽

2020 ◽

Vol 61 ◽

Author(s):

Christopher C Tisdell ◽

Zlatko Jovanoski ◽

William Guo ◽

Judith Bunder

Keyword(s):

Saudi Arabia ◽

New Zealand ◽

Mathematics Education ◽

Interest Group ◽

Applied Mathematics ◽

Special Section ◽

Mathematical Education ◽

Mathematical Methods ◽

Engineering Mathematics ◽

Technology Group

EMAC 2019 UNSW Canberra, Australia 26th Nov–29th Nov 2019 This Special Section of the ANZIAM Journal (Electronic Supplement) contains the refereed papers from the 14th Engineering Mathematics and Applications Conference (EMAC2019), which was held at the UNSW Canberra, Australia from 26th November to 29th November 2019. EMAC is held under the auspices of the Engineering Mathematics Group (EMG), which is a special interest group of the Australian and New Zealand Industrial and Applied Mathematics division of the Australian Mathematics Society. This conference provides a forum for researchers interested in the development and use of mathematical methods in engineering and applied mathematics, and aims to foster interactions between mathematicians and engineers, from both academia and industry. A further theme of the conference is the mathematical education of applied mathematicians and engineers. The event attracted participants from around the globe, including: New Zealand, Saudi Arabia, United Kingdom, Japan and Australia. The invited speakers at the 2019 meeting crossed the spectrum of specialities in engineering, mathematics, education and industry. They were: Alexander Kalloniatis (Defence Science and Technology Group), Robert K. Niven (UNSW Canberra), Katherine Seaton (La Trobe University) and Antoinette Tordesillas (University of Melbourne). All of the articles included in the EMAC 2019 Proceedings have been critically peer reviewed to the usual standards of the ANZIAM Journal. EMAC 2019 Organising Committee The conference organising committee were Fiona Richmond, Zlatko Jovanoski (Director), Leesa Sidhu, Duncan Sutherland, Fangbao Tian, Isaac Towers, Timothy Trudgian and Simon Watt. The invited speakers were chosen by a committee of experts including Alys Clark, Jennifer Flegg, Bronwyn Hajek (EMG Chair), Zlatko Jovanoski, Dann Mallet, Robert Niven, Brandon Pincombe, Melanie Roberts (Chair) and Harvinder Sidhu.

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