Theoretical architecture design of a community risk prevention big data platform
JIA Nan1, GUO Danhuai2, CHEN Yongqiang3, LIU Yi1
1. Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing 100084, China; 2. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100019, China; 3. Department of Mechanics and Engineering Science, College of Engineering, Peking University, Beijing 100871, China
Abstract：Since communities are the basic units for public safety management, community risk prevention is of great significance. Community risk prevention must first identify community risks for people, things and management. This study analyzed the characteristics and causes of community risk to identify community risk prevention methods and how to monitor, control, predict, quickly detect and prevent community risk. Current international development trends for community risk prevention are reviewed to show that big data platforms are the key technology for community risk prevention. Finally, this paper describes the function, structure and construction of a large data platform for community risk prevention. This research on community risk prevention and big data platforms provides theoretical and technical support for community safety and security.
贾楠, 郭旦怀, 陈永强, 刘奕. 面向社区风险防范的大数据平台理论架构设计[J]. 清华大学学报（自然科学版）, 2019, 59(2): 122-128.
JIA Nan, GUO Danhuai, CHEN Yongqiang, LIU Yi. Theoretical architecture design of a community risk prevention big data platform. Journal of Tsinghua University(Science and Technology), 2019, 59(2): 122-128.
[1] WEBSTER C J, GLASZE G, FRANTZ K. The global spread of gated communities[J]. Environment and Planning B:Planning and Design, 2002, 29(3):315-320. [2] BLANDY S. Fear of crime and of anti-social behaviour and their relation to the spread of residential gated communities in England[J]. Deviance et Societe, 2009, 33(4):557-572. [3] PATTERSON E B. Poverty, income inequality, and community crime rates[J]. Criminology, 1991, 29(4):755-776. [4] PITTS J. Crime Prevention and community safety:New directions[J]. Safer Communities, 2012, 1(2):46-48. [5] WANG Y. The Development of wireless personnel positioning in Internet of Things based on ZigBee and sensors[J]. International Journal of Digital Content Technology and Its Applications, 2012, 6(12):47-54. [6] BOUCHRIKA I, Carter J N, Nixon M S. Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras[J]. Multimedia Tools and Applications, 2016, 75(2):1201-1221. [7] MAO G Q, FIDAN B, Anderson B D O. Wireless sensor network localization techniques[J]. Computer Networks, 2007, 51(10):2529-2553. [8] BATISTA N C, MELICIO R, MATIAS J C O, et al. Photovoltaic and wind energy systems monitoring and building/home energy management using ZigBee devices within a smart grid[J]. Energy, 2013, 49:306-315. [9] KOVALENKO V, YAROVOY A, LIGTHART L P. Waveform based detection of anti-personnel mines with an UWB radar[C]//Proceedings of 2005 IEEE International Conference on Ultra-Wideband. Zurich, Switzerland:IEEE, 2005:644-649. [10] JOHANSEN I. Scenario modelling with morphological analysis[J]. Technological Forecasting and Social Change, 2018, 126:116-125 [11] JUN S P, YOO H S, CHOI S. Ten years of research change using Google Trends:From the perspective of big data utilizations and applications[J]. Technological Forecasting and Social Change, 2018, 130:69-87. [12] GUPTA R, GUPTA H, MOHANNIA M. Cloud computing and big data analytics:What is new from databases perspective?[M]//SRINIVASA S, BHATNAGAR V. Big Data Analytics. Berlin Heidelberg:Springer, 2012. [13] GUO D H, Du Y. A visualization platform for spatio-temporal data:A data intensive computation framework[C]//Proceedings of 201523rd International Conference on Geoinformatics. Wuhan, China:IEEE, 2016:1-6. [14] ZHANG F, ZHOU Z, XU W J, et al. Cloud manufacturing resource service platform based on intelligent perception network using fiber optic sensing[J]. International Journal on Advances in Information Sciences and Service Sciences, 2012, 4(23):366-372. [15] ALEXANDROV A, BERGMANN R, EWEN S, et al. The stratosphere platform for big data analytics[J]. The Vldb Journal, 2014, 23(6):939-964. [16] GULISANO V, JIMENEZ P R, PATIÑO-MARTINEZ M, et al. A big data platform for large scale event processing[J]. ERCIM News, 2012(89):2. [17] COMES T, WIJNGAARDS N, VAN DE WALLE B. Exploring the future:Runtime scenario selection for complex and time-bound decisions[J]. Technological Forecasting and Social Change, 2015, 97:29-46. [18] LEWIS A, OLIVER S, LVMBURNER L, et al. The Australian geoscience data cube-Foundations and lessons learned[J]. Remote Sensing of Environment, 2017, 202:276-292. [19] PHAN N, DOU D J, WANG H, et al. Ontology-based deep learning for human behavior prediction with explanations in health social networks[J]. Information Sciences, 2017, 384:298-313. [20] RIZZOLI A E, DONATELLI M, ATHANASIADIS I N, et al. Semantic links in integrated modelling frameworks[J]. Mathematics and Computers in Simulation, 2008, 78(2-3):412-423. [21] IVER B, SHANKARANARAYANAN G, LENARD M L. Model management decision environment:A Web service prototype for spreadsheet models[J]. Decision Support Systems, 2005, 40(2):283-304.
2019, Vol. 59 
