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Stream Processing Algorithms that model behavior changes

Capponi, Agostino and Chandy, Mani (2005) Stream Processing Algorithms that model behavior changes. California Institute of Technology , Pasadena, CA. (Unpublished)

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This paper presents algorithms that fuse information in multiple event streams to update models that represent system behavior. System behaviors vary over time; for example, an information network varies from heavily loaded to lightly loaded conditions; patterns of incidence of disease change at the onset of pandemics; file access patterns change from proper usage to improper use that may signify insider threat. The models that represent behavior must be updated frequently to adapt to changes rapidly; in the limit, models must be updated continuously with each new event. Algorithms that adapt to change in behavior must depend on the appropriate length of history: Algorithms that give too much weight to the distant past will not adapt to changes in behavior rapidly; algorithms that don't consider enough past information may conclude incorrectly, from noisy data, that behavior has changed while the actual behavior remains unchanged. Efficient algorithms are incremental -- the computational time required to incorporate each new event should be small and ideally independent of the length of the history.

Item Type:Report or Paper (Technical Report)
Additional Information:© 2005 California Institute of Technology.
Group:Computer Science Technical Reports
Record Number:CaltechCSTR:2005.004
Persistent URL:
Usage Policy:You are granted permission for individual, educational, research and non-commercial reproduction, distribution, display and performance of this work in any format.
ID Code:27075
Deposited By: Imported from CaltechCSTR
Deposited On:01 Apr 2005
Last Modified:07 Dec 2016 19:05

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