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http://dx.doi.org/10.3745/KIPSTD.2003.10D.2.283

A Study of Similarity Measures on Multidimensional Data Sequences Using Semantic Information  

Lee, Seok-Lyong (한국외국어대학교 산업정보시스템공학부)
Lee, Ju-Hong (인하대학교 컴퓨터공학부)
Chun, Seok-Ju (안산1대학 인터넷정보과)
Publication Information
The KIPS Transactions:PartD / v.10D, no.2, 2003 , pp. 283-292 More about this Journal
Abstract
One-dimensional time-series data have been studied in various database applications such as data mining and data warehousing. However, in the current complex business environment, multidimensional data sequences (MDS') become increasingly important in addition to one-dimensional time-series data. For example, a video stream can be modeled as an MDS in the multidimensional space with respect to color and texture attributes. In this paper, we propose the effective similarity measures on which the similar pattern retrieval is based. An MDS is partitioned into segments, each of which is represented by various geometric and semantic features. The similarity measures are defined on the basis of these segments. Using the measures, irrelevant segments are pruned from a database with respect to a given query. Both data sequences and query sequences are partitioned into segments, and the query processing is based upon the comparison of the features between data and query segments, instead of scanning all data elements of entire sequences.
Keywords
Multidimensional Data Sequence; Similarity Measure; Pattern Retrieval;
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Times Cited By KSCI : 1  (Citation Analysis)
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