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Cross-Domain Data Fusion for Disaster Detection

机译：跨域数据融合用于灾难检测

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摘要

With the advancement of the internet and the World Wide Web, staying updated with current affairs has become very easy. Every recent news, current event is just a type away. The large number of domains - whether it's a search engine, news domain or social media domain - that are coming into existing every day brings with it an abundance of information. This gives rise to two main questions. Is the information about a particular event from one domain enough? Is the information correct? The answer to the first question varies from person to person. One might just be satisfied with the result that they get from querying in one domain while others might be curious to know what other domains have to offer for a given query. This leads to the need of summarization of data from various domains. Summarization of data and high accuracy may not seem that vital for a regular event, for instance, someone querying "Cold Play Concert in the US". But it rises to importance in cases where someone queries "Earthquake in California". In scenarios where people want to monitor a disaster it becomes very useful to have information gathered from various sources and summarized in one place. Researchers all over the world have come up with cross-domain data fusion techniques for monitoring disasters. We decided to introduce a dataflow of cross-domain data fusion that gathers the raw data on current disasters from various sources, processes it, accumulates it together to give a summarized table. This approach tries to lessen the need of traversing from one domain to another to obtain information about a particular event. Also it tries to validate the summarized information based on the fact that the more the domains display the same information, the more the accuracy of the data. We evaluate the approach through the amount of relevant information from different domains.
机译：随着Internet和Internet的发展，保持时事更新变得非常容易。每个最近的新闻，时事都只有一种类型。每天都存在的大量域（无论是搜索引擎，新闻域还是社交媒体域）都带来了大量的信息。这引起了两个主要问题。来自一个域的有关特定事件的信息是否足够？信息正确吗？第一个问题的答案因人而异。一个人可能只是对它们从一个域中查询得到的结果感到满意，而其他人可能好奇地知道对于给定查询，其他域必须提供什么。这导致需要汇总来自各个域的数据。数据的汇总和高精度对于常规事件似乎并不那么重要，例如，有人查询“美国的冷战音乐会”。但是，在有人质疑“加利福尼亚地震”的情况下，这一点变得尤为重要。在人们想要监视灾难的情况下，将信息从各种来源收集并汇总到一个位置非常有用。全世界的研究人员都提出了用于监视灾难的跨域数据融合技术。我们决定引入跨域数据融合的数据流，该数据流从各种来源收集有关当前灾难的原始数据，进行处理，并将其汇总在一起以提供汇总表。这种方法试图减少从一个域遍历到另一个域以获得有关特定事件的信息的需求。它还尝试根据以下事实来验证汇总信息：域显示的信息越多，数据的准确性就越高。我们通过来自不同领域的大量相关信息来评估该方法。

著录项

  • 作者

    Ghosh, Smita.;

  • 作者单位

    The University of Texas at Dallas.;

  • 授予单位 The University of Texas at Dallas.;
  • 学科 Computer science.
  • 学位 M.S.C.S.
  • 年度 2017
  • 页码 50 p.
  • 总页数 50
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 康复医学;
  • 关键词

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