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https://doi.org/10.1...arrow_drop_down
https://doi.org/10.1007/114279...
Part of book or chapter of book . 2005 . Peer-reviewed
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DBLP
Conference object . 2017
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Processing High-Speed Intelligence Feeds in Real-Time

Authors: Alan J. Demers; Johannes Gehrke; Mingsheng Hong; Mirek Riedewald;

Processing High-Speed Intelligence Feeds in Real-Time

Abstract

Intelligence organizations face the daunting task of collecting all relevant pieces of information and to draw conclusions about potential threats in a timely manner. Typical information sources range from news tickers, financial transaction logs and message logs to satellite images and speech recordings. This wealth of data is continuously updated and arrives in high-speed data streams; it needs to be analyzed both in real-time (e.g., to estimate the importance of the information and to generate early threat alerts) and offline by sophisticated data mining tools. This work focuses on the real-time aspects of processing these massive streams of intelligence data. We also show how real-time and data mining components can interact effectively.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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