Daisy Zhe Wang and Eirinaios Chrysovalantis Michelakis and Liviu Tancau

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2009-114

August 11, 2009

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-114.pdf

Recently, wireless sensor devices have been widely deployed in various application settings (including environmental research, control systems, etc.). Because of the inherent unreliability of sensor readings, any kind of reasoning in sensor environments needs to carefully account for noise. The key goal of PCET is to build an infrastructure that can automatically infer and reason about the probabilities of triggered events, using a principled probabilistic model for the underlying sensor data. Through such probabilistic reasoning, PCET can incorporate uncertainly factors and make finer – grain decisions on event occurrences. This is achieved through the use of a Bayesian Network to directly model and exploit correlations across different sensors and the definition of a complex – event language, which allows users / applications to create hierarchies of higher-level events. As experimental results verify, PCET simplifies the development process and boosts the efficiency of any system dealing with inherently uncertain data streams.

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BibTeX citation:

@techreport{Wang:EECS-2009-114,
    Author= {Wang, Daisy Zhe and Michelakis, Eirinaios Chrysovalantis and Tancau, Liviu},
    Title= {Probabilistic Complex Event Triggering},
    Year= {2009},
    Month= {Aug},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-114.html},
    Number= {UCB/EECS-2009-114},
    Abstract= {Recently, wireless sensor devices
have been widely deployed in various application
settings (including environmental
research, control systems, etc.). Because of
the inherent unreliability of sensor readings,
any kind of reasoning in sensor environments
needs to carefully account for noise.
The key goal of PCET is to build an infrastructure
that can automatically infer and
reason about the probabilities of triggered
events, using a principled probabilistic model
for the underlying sensor data. Through such
probabilistic reasoning, PCET can incorporate
uncertainly factors and make finer –
grain decisions on event occurrences. This
is achieved through the use of a Bayesian
Network to directly model and exploit correlations
across different sensors and the
definition of a complex – event language,
which allows users / applications to create
hierarchies of higher-level events. As experimental
results verify, PCET simplifies the
development process and boosts the efficiency
of any system dealing with inherently
uncertain data streams.},
}

EndNote citation:

%0 Report
%A Wang, Daisy Zhe 
%A Michelakis, Eirinaios Chrysovalantis 
%A Tancau, Liviu 
%T Probabilistic Complex Event Triggering
%I EECS Department, University of California, Berkeley
%D 2009
%8 August 11
%@ UCB/EECS-2009-114
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-114.html
%F Wang:EECS-2009-114