Title: Rapid detection of abnormal sales data on e-commerce platforms under the digital transformation of enterprises

Authors: Jing Wang; Hongmei Zhao

Addresses: School of International Business and Economics, Hunan Sany Polytechnic College, Changsha, 410100, China ' School of International Business and Economics, Hunan Sany Polytechnic College, Changsha, 410100, China

Abstract: In order to solve the problems of long time consumption and low accuracy in existing sales data anomaly detection methods, a new e-commerce platform sales data abnormal rapid detection method is proposed under the digital transformation of enterprises. Firstly, analyse the impact of enterprise digital transformation on sales data of e-commerce platforms. Secondly, principal component analysis is used to extract key information through dimensionality reduction techniques. Singular value decomposition is utilised to process data and effectively identify the main factors affecting sales. Again, by calculating the dispersion of sales data, quantitatively evaluate the fluctuation of sales data. Finally, optimise the grid partitioning and KNN algorithm parameters, and use the fast density peak algorithm to achieve efficient and real-time abnormal detection in e-commerce platform sales data. Experimental results show that the data abnormal detection accuracy of our method consistently remains above 91%, and the longest detection time does not exceed 10 s.

Keywords: digital transformation of enterprises; e-commerce platform; sales data; rapid detection of anomalies.

DOI: 10.1504/IJBIDM.2025.149067

International Journal of Business Intelligence and Data Mining, 2025 Vol.27 No.2/3/4, pp.107 - 120

Received: 21 Oct 2024
Accepted: 16 Jan 2025
Published online: 13 Oct 2025 *

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