Two Stage Knowledge Discovery for Spatio-temporal Radio-emission Data

Authors:
Matthias Haringer;Lothar Hotz;Vera Kamp
Affiliations:
HITeC c/o Department Informatik, University of Hamburg, Germany, email: haringer@informatik.uni-hamburg.de;HITeC c/o Department Informatik, University of Hamburg, Germany, email: hotz@informatik.uni-hamburg.de;Plath GmbH, Hamburg, Germany, email: kamp@plath.de
Venue:
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Year:
2008

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Abstract

In this paper, we introduce a method to examine and interpret spatio-temporal radio emission datasets. The goal is to find communication patterns in the data in respect to spatial, temporal, and frequency based attributes. The chosen approach is a combination of two different AI-methods. First a clustering algorithm groups spatially close data points to potential emitters. In a second step a model-based constraint solving technique is applied to find relationships between the identified emitters. The used models describe rules of the communications that are to be found. This guarantees a flexible search for different kinds of communication.