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Pavel Senin 
Collaborative Software Development Laboratory 
Information and Computer Sciences, University of Hawaii, Honolulu, HI USA senin@hawaii.edu
Patterns frequency 
Variable length 
of patterns 
Superimposed rule subsequences
Jessica Lin, Xing Wang, Dept. of Computer Science, George Mason University, USA 
Tim Oates, Sunil Gandhi, Dept. of Computer Science, University of Maryland, Baltimore County, USA 
Arnold P. Boedihardjo, Crystal Chen, Susan Frankenstein, ERDC, U.S. Army Corps of Engineers, USA 
Manfred Lerner, SAP, Germany 
Pavel Senin, UH Hawaii at Manoa, USA 
Our tool implements a ‘‘rule density visualization’’, that shows, by shading the background color intensity, the amount of the grammar’ rules discovered along the input time series, allowing visual comprehension of the effective regularities in data. 
In addition, this approach enables the anomaly detection and speeds-up HOT-SAX heuristics. 
http://github.com/GrammarViz2

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GrammarViz 2.0 demo slides

  • 1. Pavel Senin Collaborative Software Development Laboratory Information and Computer Sciences, University of Hawaii, Honolulu, HI USA senin@hawaii.edu
  • 2. Patterns frequency Variable length of patterns Superimposed rule subsequences
  • 3. Jessica Lin, Xing Wang, Dept. of Computer Science, George Mason University, USA Tim Oates, Sunil Gandhi, Dept. of Computer Science, University of Maryland, Baltimore County, USA Arnold P. Boedihardjo, Crystal Chen, Susan Frankenstein, ERDC, U.S. Army Corps of Engineers, USA Manfred Lerner, SAP, Germany Pavel Senin, UH Hawaii at Manoa, USA Our tool implements a ‘‘rule density visualization’’, that shows, by shading the background color intensity, the amount of the grammar’ rules discovered along the input time series, allowing visual comprehension of the effective regularities in data. In addition, this approach enables the anomaly detection and speeds-up HOT-SAX heuristics. http://github.com/GrammarViz2