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Supporting Self-Adaptation in Streaming Data Mining Applications
 

Summary: Supporting Self-Adaptation in Streaming Data Mining
Applications
Liang Chen Gagan Agrawal
Department of Computer Science and Engineering
Ohio State University, Columbus OH 43210
chenlia,agrawalĄ @cse.ohio-state.edu
ABSTRACT
There are many application classes where the users are flexible with
respect to the output quality. At the same time, there are other con-
straints, such as the need for real-time or interactive response, which
are more crucial. This paper presents and evaluates a runtime algo-
rithm for supporting adaptive execution for such applications. The
particular domain we target is distributed data mining on streaming
data. This work has been done in the context of a middleware system
called GATES (Grid-based AdapTive Execution on Streams) that we
have been developing.
The self-adaptation algorithm we present and evaluate in this paper
has the following characteristics. First, it carefully evaluates the long-
term load at each processing stage. It considers different possibilities
for the load at a processing stage and its next stages, and decides if the

  

Source: Agrawal, Gagan - Department of Computer Science and Engineering, Ohio State University

 

Collections: Computer Technologies and Information Sciences