Discover Relaxed Periodicity in Temporal Databases.

Changjie Tang, Zhonghua Yu, Tianqing Zhang: Discover Relaxed Periodicity in Temporal Databases. DASFAA 1999: 203-209
  author    = {Changjie Tang and
               Zhonghua Yu and
               Tianqing Zhang},
  editor    = {Arbee L. P. Chen and
               Frederick H. Lochovsky},
  title     = {Discover Relaxed Periodicity in Temporal Databases},
  booktitle = {Database Systems for Advanced Applications, Proceedings of the
               Sixth International Conference on Database Systems for Advanced
               Applications (DASFAA), April 19-21, Hsinchu, Taiwan},
  publisher = {IEEE Computer Society},
  year      = {1999},
  isbn      = {0-7695-0084-6},
  pages     = {203-209},
  ee        = {db/conf/dasfaa/TangYZ99.html},
  crossref  = {DBLP:conf/dasfaa/99},
  bibsource = {DBLP,}


The Relaxed-Periodicity Pattern describes loose-cyclic behavior of objects while allowing uneven stretch or shrink on time axis, limited noises, and inflation /deflation of attribute values. To discover Relaxed-Periodicity from Temporal Databases, we propose the concepts of Attribute Trend, Trend Inertia, Peak-Valley Pattern, Inertia Algorithm with Anti-noise ability, as well as the Peak-Valley Algorithm, and show that the implementation prototype is efficient.

Copyright © 1999 by The Institute of Electrical and Electronic Engineers, Inc. (IEEE). Abstract used with permission.


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