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[Colloquium] Fast Primitives for Time Series Data Mining

February 14, 2013

  • Date: Thursday, February 14, 2013 
  • Time: 11:00 am — 11:50 am 
  • Place: Mechanical Engineering 218

Abdullah Mueen
Cloud and Information Services Lab of Microsoft 

Data mining and knowledge discovery algorithms for time series data use primitives such as bursts, motifs, outliers, periods etc. as features. Fast algorithms for finding these primitive features are usually approximate whereas exact ones are very slow and therefore never used on real data. In this talk, I present efficient and exact algorithms for two time series primitives, time series motifs and shapelets. The algorithms speed up the exact search for motifs and shapelets by efficient bounds based on triangular inequality. The algorithms are much faster than the trivial solutions and successfully discover motifs and shapelets of real time series from diverse sensors such as EEG, ECG, Accelerometers and Motion captures. I present case studies on some of these data sources and end with promising directions for new and improved primitives.


Bio: Abdullah Mueen has earned his PhD in computer science at the University of California, Riverside in 2012. His adviser was Professor Eamonn Keogh. He is primarily interested in designing primitives for time series data mining. In addition, he has experiences on working with different forms of data such as XML, DNA, spectrograms, images and trajectories. He has published his work in the top data mining conferences including KDD, ICDM and SDM. His dissertation has been selected as the runner-up in the SIGKDD Doctoral Dissertation Award in 2012. Presently he is a scientist in the Cloud and Information Services Lab of Microsoft and works on telemetry analytics.