Andrés Munoz-Arcentales, W. Velásquez, J. Salvachúa
{"title":"快速数据体系结构在应急疏散系统警报生成中的实用方法","authors":"Andrés Munoz-Arcentales, W. Velásquez, J. Salvachúa","doi":"10.1109/ISNCC.2018.8531069","DOIUrl":null,"url":null,"abstract":"This paper describes a proof of concept of a Fast-Data architecture to generate early response alerts on unforeseen events. For achieving that, in this work is presented the implementation of a fully integrated system capable to handle and process streaming data in order to generate an alert response for each generated event. The deployment stated are composed by a simulated wireless sensor network for generating environmental values, a centralized Kafka server for data segmentation and a machine learning model deployed in a Spark cluster for generating the emergency alerts. Also, a simulation was conducted assuming that a fire had affected the simulated scenario in order to determine and evaluate the system's behavior. Finally, the classification model is presented as an early system alternative based on real-time processing and can be used in different areas of occupational safety.","PeriodicalId":313846,"journal":{"name":"2018 International Symposium on Networks, Computers and Communications (ISNCC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Practical Approach of Fast-Data Architecture Applied to Alert Generation in Emergency Evacuation Systems\",\"authors\":\"Andrés Munoz-Arcentales, W. Velásquez, J. Salvachúa\",\"doi\":\"10.1109/ISNCC.2018.8531069\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes a proof of concept of a Fast-Data architecture to generate early response alerts on unforeseen events. For achieving that, in this work is presented the implementation of a fully integrated system capable to handle and process streaming data in order to generate an alert response for each generated event. The deployment stated are composed by a simulated wireless sensor network for generating environmental values, a centralized Kafka server for data segmentation and a machine learning model deployed in a Spark cluster for generating the emergency alerts. Also, a simulation was conducted assuming that a fire had affected the simulated scenario in order to determine and evaluate the system's behavior. Finally, the classification model is presented as an early system alternative based on real-time processing and can be used in different areas of occupational safety.\",\"PeriodicalId\":313846,\"journal\":{\"name\":\"2018 International Symposium on Networks, Computers and Communications (ISNCC)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 International Symposium on Networks, Computers and Communications (ISNCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISNCC.2018.8531069\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Symposium on Networks, Computers and Communications (ISNCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISNCC.2018.8531069","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Practical Approach of Fast-Data Architecture Applied to Alert Generation in Emergency Evacuation Systems
This paper describes a proof of concept of a Fast-Data architecture to generate early response alerts on unforeseen events. For achieving that, in this work is presented the implementation of a fully integrated system capable to handle and process streaming data in order to generate an alert response for each generated event. The deployment stated are composed by a simulated wireless sensor network for generating environmental values, a centralized Kafka server for data segmentation and a machine learning model deployed in a Spark cluster for generating the emergency alerts. Also, a simulation was conducted assuming that a fire had affected the simulated scenario in order to determine and evaluate the system's behavior. Finally, the classification model is presented as an early system alternative based on real-time processing and can be used in different areas of occupational safety.