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Semantic-Enabled Transformation Framework for Time Series

Paper, was published by Robert Barta and Thomas Bleier at 2009-06-01

This paper proposes to use an integrative approach for processing of time series with Topic Maps.

External Link: download paper

Conventional processing of time series is done along a split horizon: on the one hand it has to handle quantitative data organized along the time axis, on the other hand meta data capturing circumstantial facts about the values, or about the time sequence as a whole. We propose to use an integrative approach using a domain specific language for the transformation of time sequences, covering arithmetic, temporal but also semantic aspects of such computations. In that we leverage Topic Maps as one existing semantic technology.


Robert Barta

No contact information available. 


Robert is project leader of Perl XTM Engine (superseeded.. and Perl TM.

Thomas Bleier

No contact information available. 


Thomas is author of Semantic-Enabled.. and Semantically Enabled SOS.. .


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I like the easy but powerful way of merging Topic Maps to extend and combine existing knowledge bases. Thus I see high potential in distributed environments where peer to peer solutions may open the gates to the real Web 3.0.

Marcel Hoyer
Topic Maps Lab auf der Cebit 2011

Graduate from the Topic Maps Lab