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Interactive Refinement of Filtering Queries on Streaming Intelligence Data

  • Yiming Ma
  • Dawit Yimam Seid
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3975)

Abstract

Intelligence analysis involves routinely monitoring and correlating large amount of data streaming from multiple sources. In order to detect important patterns, the analyst normally needs to look at data gathered over a certain time window. Given the size of data and rate at which it arrives, it is usually impossible to manually process every record or case. Instead, automated filtering (classification) mechanisms are employed to identify information relevant to the analyst’s task. In this paper, we present a novel system framework called FREESIA (Filter REfinement Engine for Streaming InformAtion) to effectively generate, utilize and update filtering queries on streaming data.

Keywords

Relevance Feedback Streaming Data Initial Query Similarity Query Relevant Record 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yiming Ma
    • 1
  • Dawit Yimam Seid
    • 1
  1. 1.School of Information and Computer ScienceUniversity of CaliforniaIrvineUSA

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