GrammarViz 2.0: A Tool for Grammar-Based Pattern Discovery in Time Series

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8726)


The problem of frequent and anomalous patterns discovery in time series has received a lot of attention in the past decade. Addressing the common limitation of existing techniques, which require a pattern length to be known in advance, we recently proposed grammar-based algorithms for efficient discovery of variable length frequent and rare patterns. In this paper we present GrammarViz 2.0, an interactive tool that, based on our previous work, implements algorithms for grammar-driven mining and visualization of variable length time series patterns1.


Context Free Grammar Pattern Discovery Grammar Rule Input Time Series Merging Operator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.ICS Dept., CSDLUniversity of Hawaii,ManoaUSA
  2. 2.Dept. of Computer ScienceGeorge Mason UniversityUSA
  3. 3.Dept. of Computer ScienceUniversity of Maryland, Baltimore CountyUSA
  4. 4.Engineer Research and Development CenterU.S. Army Corps of EngineersUSA
  5. 5.SAPGermany

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