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Construction of Public Security Rapid Response Communication and Command System Based on Spatiotemporal Big Data

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DOI: 10.23977/acss.2023.070606 | Downloads: 13 | Views: 345

Author(s)

Chang Liu 1

Affiliation(s)

1 Chaoyang District Public Security Bureau, 645 West Minzhu Street, Changchun, Jilin, 130000, China

Corresponding Author

Chang Liu

ABSTRACT

Spatial and temporal big data is one of the most important types of big data, and spatiotemporal big data is the foundation for accurately measuring and extracting the value of data content. The disadvantage of traditional data representation is that it cannot cope with the rapidly growing amount of data, and its most important attribute is its global ability to represent big data. In the era of big data, data has complex relationships, and the main value of spatiotemporal big data lies in the relationships between time, space, and things. However, the complexity of spatiotemporal big data and the dynamic evolution between them make it difficult to represent and calculate relationships. The value of spatiotemporal big data lies in discovering and utilizing the hidden laws behind it. The unique value of spatiotemporal big data lies in that, unlike local data, it contains information about significant large-scale events that are particularly difficult to understand due to their large spatial scope, complex measures, and behaviors.

KEYWORDS

Spatiotemporal Big Data, Public Security Rapid Response, Communication Command, System Construction

CITE THIS PAPER

Chang Liu, Construction of Public Security Rapid Response Communication and Command System Based on Spatiotemporal Big Data. Advances in Computer, Signals and Systems (2023) Vol. 7: 45-53. DOI: http://dx.doi.org/10.23977/acss.2023.070606.

REFERENCES

[1] Hassan M H, Jubair M A, Mostafa S A. Analyzing bit error rate of relay sensors selection in wireless cooperative communication systems. Bulletin of Electrical Engineering and Informatics, 2021, 10(1):216-223. 
[2] Chen Y. A., Zhang Q., Chen T. Y., Cai W. Q., Liao S. K., Zhang J.,... & Pan J. W.. An integrated space-to-ground quantum communication network over 4,600 kilometres. Nature, 2021, 589(7841), 214-219. 
[3] Zappone A., Di Renzo M., & Debbah M. Wireless networks design in the era of deep learning: Model-based, AI-based, or both? IEEE Transactions on Communications, 2019, 67(10), 7331-7376. 
[4] Huang B., & Wang J. Big spatial data for urban and environmental sustainability. Geo-spatial Information Science, 2020, 23(2), 125-140. 
[5] Shashidharan A, Chandola V, Vatsavai R R. The 9th ACM SIGSPATIAL International Workshop on Analytics for Big Spatial Data (BigSpatial 2020): November 3, 2020. SIGSPATIAL Special, 2021, 12(3):15-16. 
[6] Shashidharan A, Chandola V, Vatsavai R R. The Eighth ACM SIGSPATIAL International Workshop on Analysis for Big Spatial Data: Chicago, IL, USA - November 5, 2019. SIGSPATIAL Special, 2020, 11(3):38-39. 
[7] Eken S, Sayar A. A MapReduce-Based Big Spatial Data Framework for Solving the Problem of Covering a Polygon with Orthogonal Rectangles. Tehnicki Vjesnik, 2019, 26(1):36-42. 
[8] Sabek I, Mokbel M F. Machine learning meets big spatial data. Proceedings of the VLDB Endowment, 2019, 12(12):1982-1985. 
[9] Martinez-Alvarez F, Morales-Esteban A. Big data and natural disasters: New approaches for spatial and temporal massive data analysis. Computers & Geosciences, 2019, 129(AUG):38-39. 
[10] Sivakumar K. Spatial Data Mining: Recent Trends in the Era of Big Data. Journal of Advanced Research in Dynamical and Control Systems, 2020, 12(SP7):912-916. 
[11] Jo J H. A Sampling Approach for Visualization of Spatial Big Data. Journal of Korean Society for Geospatial Information Science, 2020, 28(4):89-97. 
[12] Yi C. Directional Difference of the Residential Relocation among the Age Groups Using Spatial Big Data Analysis. Journal of Korea Planning Association, 2020, 55(1):98-111. 
[13] Yu S C, Dong B S, Ahn J W. A Study on Spatial Analysis of Greenhouse Gas Emissions in Building Sector Used by the Spatial Big Data in case of Seoul. Journal of Korean Society for Geospatial Information Science, 2019, 27(4):11-19. 
[14] Bello R W, Talib A, Mohamed A. A Framework for Real-time Cattle Monitoring using Multimedia Networks. International Journal of Recent Technology and Engineering, 2020, 8(5):974-979. 
[15] Kumar J, Ammar M, Kantilal S A, et al. Advanced Data Storage Security System for Public Cloud. International Journal of Fog Computing, 2020, 3(2):21-30. 
[16] Kim K K. A Survey on the Recognition of Field Police Officers on the Uniform Autonomous Police System. Korean Police Studies Review, 2020, 19(4):3-20. 
[17] Park D K, Lee H J. A Study on Introduction of Municipal Police System in Korea. Korean Police Studies Review, 2019, 18(1):55-92. 
[18] Hoon K S. A Study of Two Korea's Police Integration Based on Local Municipal Police System. Journal of Peace and Unification Studies, 2019, 11(2):399-446. 
[19] Xu R, Nikouei Y, Nagothu D, et al. BlendSPS: A BLockchain-ENabled Decentralized Smart Public Safety System. Smart Cities, 2020, 3(3):928-951. 
[20] Snyder A. Fitting Into the. Public safety communications, 2019, 85(1):36-37.

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