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.
This research was supported by the National Science Foundation under Award Numbers 0331707 and 0331690.
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© 2006 Springer-Verlag Berlin Heidelberg
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Ma, Y., Seid, D.Y. (2006). Interactive Refinement of Filtering Queries on Streaming Intelligence Data. In: Mehrotra, S., Zeng, D.D., Chen, H., Thuraisingham, B., Wang, FY. (eds) Intelligence and Security Informatics. ISI 2006. Lecture Notes in Computer Science, vol 3975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760146_4
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DOI: https://doi.org/10.1007/11760146_4
Publisher Name: Springer, Berlin, Heidelberg
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