Abstract
In modern society , various forms of crime are constantly occurring. Accordingly, several safe-return systems for the socially vulnerable are being developed. However, those systems are mainly focused on responding to dangerous situations that have already occurred, and they do not predict the possibility of crime reflected by information about the user’s surroundings in real time. This paper proposes a new safe-return-home service that allows users to be notified of, and therefore handle, the possible dangerous situations surrounding them in real time. This is accomplished by collecting and analyzing various types of big data about the user’s surroundings in real time. Collected and analyzed data include the locations of users, the locations of CCTV (Closed-Circuit Television) cameras, crime/disaster/accident-related real-time news data, the locations of shelters, real-time CCTV video data, and social network service data. Through the analysis of these data, the prediction of potential surrounding dangers is visualized on user devices, and ideas for counteracting those dangers are suggested to users in real time.
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Acknowledgements
This research was financially supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-2013-0-00881) supervised by the IITP (Institute for Information and Communications Technology Promotion).
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Ryu, GA., Lee, JW., Jeong, JS., Kim, M., Yoo, KH. (2019). Real-Time Smart Safe-Return-Home Service Based on Big Data Analytics. In: Lee, W., Leung, C. (eds) Big Data Applications and Services 2017. BIGDAS 2017. Advances in Intelligent Systems and Computing, vol 770. Springer, Singapore. https://doi.org/10.1007/978-981-13-0695-2_19
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DOI: https://doi.org/10.1007/978-981-13-0695-2_19
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