Capponi, Agostino and Chandy, Mani (2005) Stream Processing Algorithms that model behavior changes. California Institute of Technology , Pasadena, CA. (Unpublished) http://resolver.caltech.edu/CaltechCSTR:2005.004
- Submitted Version
See Usage Policy.
Use this Persistent URL to link to this item: http://resolver.caltech.edu/CaltechCSTR:2005.004
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|
|Usage Policy:||You are granted permission for individual, educational, research and non-commercial reproduction, distribution, display and performance of this work in any format.|
|Deposited By:||Imported from CaltechCSTR|
|Deposited On:||01 Apr 2005|
|Last Modified:||07 Dec 2016 19:05|
Repository Staff Only: item control page