Dec 4, 2012

Mining Data Streams

Tonight SVForum (Silicon Valley Forum) and its Business Intelligence SIG will present a talk on stream data computing. If I found some interesting content there, I will report it on this blog.

Stream data are fascinating, as they are very different from traditional data we normally get from database. Examples of stream data are: data sent by wireless sensors, RFID data, heart rate monitoring, and network traffic. Such data are large volume and they come in constantly. Thus there is inherent time component in stream data.

Typical stream data mining tried to find "frequent sequential patterns". One of such patterns is called Motif. A motif is a data sub-sequence that repeats throughout.  When we apply motif discovery to heart monitoring data, we get the following:

The green ones are repeated throughout, and blue ones are also motif. The following is two dance sub-sequences that are similar to each other. They correspond to the Jackson move.

For more in-depth discussion on motif discover, check out these two papers:
1. Mueen, Abdullah, and Eamonn Keogh. "Online discovery and maintenance of time series motifs." KDD 2010
2. Jingjing Meng (and others) "Mining Motifs from Human Motions", EuroGraphics 2008


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