DDR: an index method for large time-series datasets

An, Jiyuan, Chen, Yi-Ping Phoebe and Chen, Hanxiong 2005, DDR: an index method for large time-series datasets, Information systems, vol. 30, no. 5, pp. 333-348, doi: 10.1016/j.is.2004.05.001.

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Title DDR: an index method for large time-series datasets
Author(s) An, Jiyuan
Chen, Yi-Ping Phoebe
Chen, Hanxiong
Journal name Information systems
Volume number 30
Issue number 5
Start page 333
End page 348
Publisher Pergamon
Place of publication Oxford, England
Publication date 2005
ISSN 0306-4379
Keyword(s) time series
dimensionality reduction
Summary The tree index structure is a traditional method for searching similar data in large datasets. It is based on the presupposition that most sub-trees are pruned in the searching process. As a result, the number of page accesses is reduced. However, time-series datasets generally have a very high dimensionality. Because of the so-called dimensionality curse, the pruning effectiveness is reduced in high dimensionality. Consequently, the tree index structure is not a suitable method for time-series datasets. In this paper, we propose a two-phase (filtering and refinement) method for searching time-series datasets. In the filtering step, a quantizing time-series is used to construct a compact file which is scanned for filtering out irrelevant. A small set of candidates is translated to the second step for refinement. In this step, we introduce an effective index compression method named grid-based datawise dimensionality reduction (DRR) which attempts to preserve the characteristics of the time-series. An experimental comparison with existing techniques demonstrates the utility of our approach.
Language eng
DOI 10.1016/j.is.2004.05.001
Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2004, Elsevier Ltd.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30003046

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