Pacific Institute for the Mathematical Sciences - PIMS

The problem of streaming data has gained importance in recent years
because of advances in hardware technology. The ubiquitous presence of
data streams in a number of practical domains has generated a lot of
research in this area. Example applications include surveillance for
terrorist attack, network monitoring for intrusion detection, and
others. Problems such as data mining which have been widely studied for
traditional data sets cannot be easily solved for the data stream
domain. This is because the large volume of data arriving in a stream
renders most algorithms too inefficient as these 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. Furthermore, we
also need to consider the problem of resource allocation in processing
data streams. Due to the large volume and the high speed of streaming
data, stream algorithms must cope with the effects of system overload.
Thus, how to achieve optimum results under various resource constraints
becomes a challenging task. In this talk, I’ll provide an overview,
discuss the issues and focus on how to process data streams and perform
resource adaptive computation.

PIMS/SFU Computing Science DISTINGUISHED LECTURE SERIES 2007