Real-Time Monitoring and Surveillance using Data Stream Mining
by Philip S. Yu, IBM T.J. Watson Research Center, USA
With the advance of data gathering and communication technologies, it
becomes increasingly possible to support real-time monitoring of large
amount of information from diverse information sources. Examples
include trade surveillance for security fraud and money laundering,
network monitoring for intrusion detection, bio-surveillance for
terrorist attacks, and various sensor network based monitoring
applications. Data is viewed as a continuous stream in this kind of
applications. Problems such as data mining which have been widely
studied for traditional data sets cannot be easily applied to the data
stream domain. This is because the large volume of data arriving in a
stream renders most algorithms too inefficient as most mining
algorithms require multiple scans of data which is unrealistic for
stream data. More importantly, the characteristics of the data stream
can change over time and the evolving pattern needs to be captured.
In this talk, I'll provide an overview, discuss the issues and focus
on how to mine evolving data streams.
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