Abstract
With the rapid development of information and communication technologies, massive amounts of data continue to be generated and flood all aspects of society. As one of the key departments of the government, the public security bureau masters all kinds of heterogonous data. Deep analysis of these data will help to detect and prevent public security cases and maintain social stability. Therefore, it is an urgent demand for grassroots police officers to better manage and use these data. To address this demand, in this paper, we present the work of designing and implementing a customized data modeling platform. With the modeling platform, which owns a visual interface, police officers can have a better overview and understanding of collected data and use the drag-and-drop method to build data analysis models. As a core component of this modeling platform, after analyzing 211 tables of practical police data, we built a public security domain knowledge model. Cooperating with the Sucheng branch of Suqian Public Security Bureau, we conducted a set of experiments with police officers on real police data. Experiment results show that the modeling platform has better user-friendliness and outperforms the traditional SQL-based querying method considering the integrity of querying results.
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Acknowledgement
The authors would like to thank the policemen from Sucheng branch of Suqian Public Security Bureau for their cooperation and assistance. This work was partially supported by the Shandong Provincial Natural Science Foundation (No. ZR2021MF026).
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Wang, T., Jiang, H., Zhang, H., Yan, X. (2023). A Knowledge-Enabled Customized Data Modeling Platform Towards Intelligent Police Applications. In: Li, B., Yue, L., Tao, C., Han, X., Calvanese, D., Amagasa, T. (eds) Web and Big Data. APWeb-WAIM 2022. Lecture Notes in Computer Science, vol 13421. Springer, Cham. https://doi.org/10.1007/978-3-031-25158-0_11
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