Dynamic Interaction and Visualization Design of Database Information Based on Artificial Intelligence

Yin Fan
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Abstract

With the explosive growth of data, people's demand for data analysis has become more intense. Although modern technology can collect a large amount of data, the collected original data is often useless and contains little information. How to extract useful information from massive amounts of data has become an urgent problem. Driven by artificial intelligence (AI) technology and personalized consumption demand of users, this article puts forward a dynamic interactive and visualization algorithm of e-business database information based on an improved collaborative filtering (CF) algorithm to help enterprises more efficiently mine the required potential customer groups from massive customer data and log data. Experiment results prove the effectiveness of the model and algorithm. Data mining (DM) technology is applied to the user access control model in this model. First, the maximum forward reference sequence of mobile e-business groups is mined by data technology. Then a user access control model is established according to this sequence to control user access so enterprises can formulate reasonable marketing strategies based on this knowledge.
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基于人工智能的数据库信息动态交互与可视化设计
随着数据的爆炸式增长,人们对数据分析的需求也越来越强烈。虽然现代技术可以收集到大量的数据,但收集到的原始数据往往是无用的,包含的信息很少。如何从海量数据中提取有用信息已成为一个亟待解决的问题。本文在人工智能(AI)技术和用户个性化消费需求的驱动下,提出了一种基于改进协同过滤(CF)算法的电子商务数据库信息动态交互可视化算法,帮助企业从海量客户数据和日志数据中更高效地挖掘出所需的潜在客户群。实验结果证明了该模型和算法的有效性。该模型将数据挖掘(DM)技术应用于用户访问控制模型。首先,利用数据技术挖掘移动电子商务群体的最大前向参考序列。然后按照这个顺序建立用户访问控制模型,对用户访问进行控制,企业可以根据这些知识制定合理的营销策略。
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