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A vector-geometry based spatial kNN-algorithm for traffic frequency predictions

 
: May, M.; Hecker, D.; Körner, C.; Scheider, S.; Schulz, D.

:
Postprint urn:nbn:de:0011-n-1016254 (347 KByte PDF)
MD5 Fingerprint: c95fcb3bd7984473b2e873efbd79e681
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Created on: 9.9.2010


Bonchi, F. ; Institute of Electrical and Electronics Engineers -IEEE-:
IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008. Proceedings. Vol.1 : 15 - 19 December 2008, Pisa, Italy
Piscataway, NJ: IEEE, 2008
ISBN: 978-1-4244-3903-4
pp.442-447
International Conference on Data Mining (ICDM) <8, 2008, Pisa>
Workshop on Spatial and Spatio-Temporal Data Mining (SSTDM) <2008, Pisa>
English
Conference Paper, Electronic Publication
Fraunhofer IAIS ()
spatial data mining; s-kNN; vector data; dynamic calculation; traffic frequency

Abstract
We introduce s-kNN, a nearest neighbor based spatial data mining algorithm. It belongs to the class of vector-geometry based algorithms that reason on complex spatial objects instead of point measurements. In contrast to most methods in this class, it does on the fly spatial computations that cannot be replaced by a preprocessing step without sacrificing efficiency. The key is a partial evaluation scheme for efficient computations. The algorithm is fully integrated into an object-relational spatial database. It is the basis for traffic frequency predictions (vehicles and pedestrians) for all German cities larger than 50,000 inhabitants and is the basis for pricing of posters in Germany.

: http://publica.fraunhofer.de/documents/N-101625.html