[Dmbu-l] Online Discovery and Maintenance of Time Series Motif [TOMORROW 10/18 @ 12:00 pm in MCS 148]
cmav at bu.edu
Tue Oct 18 23:37:29 EDT 2011
This is a reminder for tomorrow's talk.
Wednesday, October 18, 2011
12:00 pm -13:00 pm
Haohan Zhu will present us the following paper
*Online Discovery and Maintenance of Time Series Motif*
by Abdullah Mueen and Eamonn Keogh at University of California, Riverside.
The paper was published in KDD 2010.
The detection of repeated subsequences, time series motifs, is a problem
which has been shown to have great utility for several higher level data
mining algorithms, including classification, clustering, segmentation,
forecasting, and rule discovery. In recent years there has been significant
research effort spent on efficiently discovering these motifs in static
offline databases. However, for many domains, the inherent streaming nature
of time series demands online discovery and maintenance of time series
motifs. In this paper, we develop the first online motif discovery algorithm
which monitors and maintains motifs exactly in real time over the most
recent history of a stream. Our algorithm has a worst case update time which
is linear to the window size and is extendible to maintain more complex
pattern structures. In contrast, the current offline algorithms either need
significant update time or require very costly pre processing steps which
online algorithms simply cannot afford.
The support webpage is: http://www.cs.ucr.edu/~mueen/OnlineMotif/index.html
You can download it from: http://www.cs.ucr.edu/~mueen/pdf/sigkdd10.pdf
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