Digital Management Strategy of Natural Resource Archives Under Smart City Space-Time Big Data Platform

IF 0.5 4区计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING

International Journal of Data Warehousing and Mining Pub Date : 2023-03-24 DOI:10.4018/ijdwm.320649

Y. Wang, Pin Lv

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引用次数: 0

Abstract

The data under the smart city spatio-temporal big data platform is very diverse, and there are many modern spatial and spatial databases in the archives management system related to natural resources. Big data can effectively improve the quality and classification of natural resource archives management (referred to as NRAM for convenience of description). However, the traditional NRAM method and informatization level can no longer meet the needs of the current NRAM, so people must continue to make efforts to digitize the natural resource archives. To this end, this paper analyzed the characteristics and problems of NRAM and then used the big data platform to make corresponding management adjustments to promote the development of NRAM. Under big data, the degree of management improvement and management efficiency were better than the original NRAM, and the degree of management improvement was 14% higher than the original NRAM. In short, both big data and artificial intelligence can improve the integrated management of natural resource archives.

查看原文本刊更多论文

智慧城市时空大数据平台下的自然资源档案数字化管理策略

智慧城市时空大数据平台下的数据非常多样化，在与自然资源相关的档案管理系统中存在许多现代空间数据库。大数据可以有效提高自然资源档案管理(为便于描述，简称NRAM)的质量和分类。然而，传统的NRAM方法和信息化水平已经不能满足当前NRAM的需要，因此人们必须继续努力实现自然资源档案的数字化。为此，本文分析了NRAM的特点和存在的问题，然后利用大数据平台进行相应的管理调整，促进NRAM的发展。在大数据下，管理改进程度和管理效率均优于原NRAM，管理改进程度比原NRAM高14%。总之，大数据和人工智能都可以提高自然资源档案的综合管理。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Data Warehousing and Mining COMPUTER SCIENCE, SOFTWARE ENGINEERING-

CiteScore

2.40

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： The International Journal of Data Warehousing and Mining (IJDWM) disseminates the latest international research findings in the areas of data management and analyzation. IJDWM provides a forum for state-of-the-art developments and research, as well as current innovative activities focusing on the integration between the fields of data warehousing and data mining. Emphasizing applicability to real world problems, this journal meets the needs of both academic researchers and practicing IT professionals.The journal is devoted to the publications of high quality papers on theoretical developments and practical applications in data warehousing and data mining. Original research papers, state-of-the-art reviews, and technical notes are invited for publications. The journal accepts paper submission of any work relevant to data warehousing and data mining. Special attention will be given to papers focusing on mining of data from data warehouses; integration of databases, data warehousing, and data mining; and holistic approaches to mining and archiving