Cross-Domain Data Fusion for Disaster Detection

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Cross-Domain Data Fusion for Disaster Detection

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Title: Cross-Domain Data Fusion for Disaster Detection
Author(s):
Ghosh, Smita
Advisor: Wu, Weili
Date Created: 2017-05
Format: Thesis
Keywords: Multisensor data fusion
Disasters—Press coverage
News Web sites
Abstract: 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.
Degree Name: MSCS
Degree Level: Masters
Persistent Link: http://hdl.handle.net/10735.1/5466
Type : text
Degree Program: Computer Science

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