{"title":"一种实时突发检测方法","authors":"Ryohei Ebina, Kenji Nakamura, S. Oyanagi","doi":"10.1109/ICTAI.2011.177","DOIUrl":null,"url":null,"abstract":"Real-time burst detection over multiple window size is useful for analyzing data streams. Various burst detection methods have been proposed. However, they are not effective for real-time detection. This work proposes a new burst detection method that reduces computation by avoiding redundant data updates. It analyses an event on its occurrence, and detects the period where arrival frequency rises rapidly to the previous period. In addition, it reduces computation by suppressing data within a certain period even in the case of emergent increase of events. The effectiveness of the proposed method is evaluated by experiments with real data.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":"{\"title\":\"A Real-Time Burst Detection Method\",\"authors\":\"Ryohei Ebina, Kenji Nakamura, S. Oyanagi\",\"doi\":\"10.1109/ICTAI.2011.177\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Real-time burst detection over multiple window size is useful for analyzing data streams. Various burst detection methods have been proposed. However, they are not effective for real-time detection. This work proposes a new burst detection method that reduces computation by avoiding redundant data updates. It analyses an event on its occurrence, and detects the period where arrival frequency rises rapidly to the previous period. In addition, it reduces computation by suppressing data within a certain period even in the case of emergent increase of events. The effectiveness of the proposed method is evaluated by experiments with real data.\",\"PeriodicalId\":332661,\"journal\":{\"name\":\"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"23\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICTAI.2011.177\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTAI.2011.177","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Real-time burst detection over multiple window size is useful for analyzing data streams. Various burst detection methods have been proposed. However, they are not effective for real-time detection. This work proposes a new burst detection method that reduces computation by avoiding redundant data updates. It analyses an event on its occurrence, and detects the period where arrival frequency rises rapidly to the previous period. In addition, it reduces computation by suppressing data within a certain period even in the case of emergent increase of events. The effectiveness of the proposed method is evaluated by experiments with real data.