@article{Ong2005,
title = {Agents and stream data mining: a new perspective},
author = {Kok-Leong Ong and Zili Zhang and Wee Keong Ng and Ee-Peng Lim},
journal = {IEEE Intelligent Systems},
month = {May/June},
number = {3},
pages = {60--67},
url = {http://www3.it.deakin.edu.au/~leong/papers/is.pdf},
volume = {20},
year = {2005},
abstract = {Many organizations struggle with what to do with the massive amount
of data they collect. Although some have touted data mining as the
solution, it has failed to have a major impact despite its successes
in many areas. One reason is that data mining algorithms weren't
designed for the real world-that is, they usually assume a static
view of the data and a stable execution environment with abundant
resources. The reality, however, is that data constantly change and
the execution environment is dynamic. So, it becomes difficult for
data mining to truly deliver timely and relevant results. The solution
to this might be to combine stream data mining algorithms with intelligent
agents, as preliminary results from the Matrix project suggest.},
keywords = {Agents DataStream }
}
::