Title: Analysis method for structured big data feature based on hypernetwork model

Authors: Shu Xu

Addresses: College of Information and Electronic Engineering, Hunan City University, Yiyang, Hunan 413000, China

Abstract: In view of the problems existing in current big data feature analysis methods, such as long data feature retrieval delay, low comparison fitting index and low success rate of abnormal data extraction, this paper proposes a structured big data feature method based on the hypernetwork model. Based on the commonality of different components, the abnormal data features in big data are extracted. The characteristics of structured big data are analysed comprehensively by using the hypernetwork model, and the comprehensive data classification is carried out. The hypernetwork model is adopted to realise the structural and clustering analysis of big data features, so as to ensure the quality of data feature analysis. The experimental results show that the proposed method has shorter data feature retrieval delay, the comparison fitting index is basically above 0.9, the fitting effect is better than the literature method, and the success rate of abnormal data extraction is higher.

Keywords: hypernetwork model; structured; data characteristics; anomaly detection.

DOI: 10.1504/IJIPT.2021.117416

International Journal of Internet Protocol Technology, 2021 Vol.14 No.3, pp.162 - 168

Received: 10 Apr 2019
Accepted: 12 Jan 2020
Published online: 06 Sep 2021 *

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