{"title":"基于位置相关语料库的改进突发实时事件检测","authors":"J. Nützel, Frank Zimmermann","doi":"10.1109/FiCloud.2015.131","DOIUrl":null,"url":null,"abstract":"This paper describes an approach to detect automatically events in real-time by analyzing big data streams coming from a social network. The social network Twitter will be used for experiments because of its easy to use API and messages having positioning data. The described event detection algorithm is a burst-based event detection methods extended by a dynamically adapted reference corpus. The usage of location dependent corpora allows also detecting the location of the found events.","PeriodicalId":182204,"journal":{"name":"2015 3rd International Conference on Future Internet of Things and Cloud","volume":"89 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Improved Burst Based Real-Time Event Detection Using Location Dependent Corpora\",\"authors\":\"J. Nützel, Frank Zimmermann\",\"doi\":\"10.1109/FiCloud.2015.131\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes an approach to detect automatically events in real-time by analyzing big data streams coming from a social network. The social network Twitter will be used for experiments because of its easy to use API and messages having positioning data. The described event detection algorithm is a burst-based event detection methods extended by a dynamically adapted reference corpus. The usage of location dependent corpora allows also detecting the location of the found events.\",\"PeriodicalId\":182204,\"journal\":{\"name\":\"2015 3rd International Conference on Future Internet of Things and Cloud\",\"volume\":\"89 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-08-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 3rd International Conference on Future Internet of Things and Cloud\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FiCloud.2015.131\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 3rd International Conference on Future Internet of Things and Cloud","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FiCloud.2015.131","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improved Burst Based Real-Time Event Detection Using Location Dependent Corpora
This paper describes an approach to detect automatically events in real-time by analyzing big data streams coming from a social network. The social network Twitter will be used for experiments because of its easy to use API and messages having positioning data. The described event detection algorithm is a burst-based event detection methods extended by a dynamically adapted reference corpus. The usage of location dependent corpora allows also detecting the location of the found events.