Efficient multi-event monitoring using built-in search engines

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• 作者：Zhaoman Zhong ; Zongtian Liu ; Yun Hu ; Cunhua Li
• 关键词：information retrieval ; event retrieval ; event monitoring ; BSEs ; event rule reduction
• 刊名：Frontiers of Computer Science in China
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：10
• 期：2
• 页码：281-291
• 全文大小：524 KB
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• 作者单位：Zhaoman Zhong (1)
  Zongtian Liu (2)
  Yun Hu (1)
  Cunhua Li (1)
  
  1. School of Computer Engineering, Huaihai Institute of Technology, Lianyungang, 222006, China
  2. School of Computer Engineering and Science, Shanghai University, Shanghai, 200072, China
  
• 刊物类别：Computer Science
• 刊物主题：Computer Science, general
  Chinese Library of Science
  
• 出版者：Higher Education Press, co-published with Springer-Verlag GmbH
• ISSN：1673-7466

文摘

Users of the internet often wish to follow certain news events, and the interests of these users often overlap. General search engines (GSEs) cannot be used to achieve this task due to incomplete coverage and lack of freshness. Instead, a broker is used to regularly query the built-in search engines (BSEs) of news and social media sites. Each user defines an event profile consisting of a set of query rules called event rules (ERs). To ensure that queries match the semantics of BSEs, ERs are transformed into a disjunctive normal form, and separated into conjunctive clauses (atomic event rules, AERs). It is slow to process all AERs on BSEs, and can violate query submission rate limits. Accordingly, the set of AERs is reduced to eliminate AERs that are duplicates, or logically contained by other AERs. Five types of event are selected for experimental comparison and analysis, including natural disasters, accident disasters, public health events, social security events, and negative events of public servants. Using 12 BSEs, 85 ERs for five types of events are defined by five users. Experimental comparison is conducted on three aspects: event rule reduction ratio, number of collected events, and that of related events. Experimental results in this paper show that event rule reduction effectively enhances the efficiency of crawling.