Scenario Discovery Using Nonnegative Tensor Factorization

  • Brett W. Bader
  • Andrey A. Puretskiy
  • Michael W. Berry
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5197)


In the relatively new field of visual analytics there is a great need for automated approaches to both verify and discover the intentions and schemes of primary actors through time. Data mining and knowledge discovery play critical roles in facilitating the ability to extract meaningful information from large and complex textual-based (digital) collections. In this study, we develop a mathematical strategy based on nonnegative tensor factorization (NTF) to extract and sequence important activities and specific events from sources such as news articles. The ability to automatically reconstruct a plot or confirm involvement in a questionable activity is greatly facilitated by our approach. As a variant of the PARAFAC multidimensional data model, we apply our NTF algorithm to the terrorism-based scenarios of the VAST 2007 Contest data set to demonstrate how term-by-entity associations can be used for scenario/plot discovery and evaluation.


nonnegative tensor factorization PARAFAC scenario discovery VAST 2007 visual analytics 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Brett W. Bader
    • 1
  • Andrey A. Puretskiy
    • 2
  • Michael W. Berry
    • 2
  1. 1.Sandia National LaboratoriesAlbuquerqueUSA
  2. 2.Department of Electrical Engineering and Computer ScienceUniversity of TennesseeKnoxvilleUSA

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