Year: 2024
Type: article
Abstract: With the continuous expansion of data scale in the field of public safety, how to efficiently mine and utilize massive, multi-source, and heterogeneous data to achieve intelligent analysis and decision-making is an important topic currently faced. The complex data environment and application scenarios in this field also pose numerous challenges for the practical application of algorithmic models. Therefore, this paper focused on the specific needs of the public safety field and researched and designed a low-code data analysis and modeling platform for public safety. Based on the concept of low-code, this platform significantly reduced the difficulty of use for users. It constructed a unified data integration and modeling module, enabling standardized management of multi-source heterogeneous data, and designed an intelligent graphical data comparison module that supports data comparison analysis across multiple dimensions and visualizes the results. Furthermore, the research also explored and designed aspects such as the visual expression and collaborative sharing of result analysis, as well as the low-code application of artificial intelligence algorithms. Therefore, this study effectively lowered the threshold for data analysis applications in the field of public safety and provides strong support for the digitization of public safety.
Authors: Xinmeng Wang, Shuifeng Zhang, Jingwei Wu
Language: English
Topic: AI and Big Data Applications
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4406775433