Discovering Sentinel Rules for Business Intelligence

  • Morten Middelfart
  • Torben Bach Pedersen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5690)


This paper proposes the concept of sentinel rules for multi-dimensional data that warns users when measure data concerning the external environment changes. For instance, a surge in negative blogging about a company could trigger a sentinel rule warning that revenue will decrease within two months, so a new course of action can be taken. Hereby, we expand the window of opportunity for organizations and facilitate successful navigation even though the world behaves chaotically. Since sentinel rules are at the schema level as opposed to the data level, and operate on data changes as opposed to absolute data values, we are able to discover strong and useful sentinel rules that would otherwise be hidden when using sequential pattern mining or correlation techniques. We present a method for sentinel rule discovery and an implementation of this method that scales linearly on large data volumes.


Association Rule Sequential Pattern Pattern Mining Correlation Technique Business Intelligence 
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 2009

Authors and Affiliations

  • Morten Middelfart
    • 1
  • Torben Bach Pedersen
    • 2
  1. 1.TARGIT A/SHjørringDenmark
  2. 2.Department of Computer ScienceAalborg UniversityAalborg ØDenmark

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