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.

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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
1873-6076
Keyword(s) time series
indexing
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
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

Document type: Journal Article
Collection: School of Information Technology
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