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San Diego, California
Feb. 28, 2000 to Mar. 3, 2000
ISBN: 0-7695-0506-6
pp: 599
Seok-Lyong Lee , Korea Advanced Institute of Science and Technology
Seok-Ju Chun , Korea Advanced Institute of Science and Technology
Deok-Hwan Kim , Korea Advanced Institute of Science and Technology
Ju-Hong Lee , Korea Advanced Institute of Science and Technology
Chin-Wan Chung , Korea Advanced Institute of Science and Technology
ABSTRACT
Time-series data, which are a series of one-dimensional real numbers, have been studied in various database applications. In this paper, we extend the traditional similarity search methods on time-series data to support a multidimensional data sequence, such as a video stream. We investigate the problem of retrieving similar multi- dimensional data sequences from a large database. To prune irrelevant sequences in a database, we introduce correct and efficient similarity functions. Both data sequences and query sequences are partitioned into subsequences, and each of them is represented by a Minimum Bounding Rectangle (MBR). The query processing is based upon these MBRs, instead of scanning data elements of entire sequences.Our method is designed (1) to select candidate sequences in a database, and (2) to find the subsequences of a selected sequence, each of which falls under the given threshold. The latter is of special importance in the case of retrieving subsequences from large and complex sequences such as video. By using it, we do not need to browse the whole of the selected video stream, but just browse the sub-streams to find a scene we want. We have performed an extensive experiment on synthetic, as well as real data sequences (a collection of TV news, dramas, and documentary videos) to evaluate our proposed method. The experiment demonstrates that 73-94 percent of irrelevant sequences are pruned using the proposed method, resulting in 16-28 times faster response time compared with that of the sequential search.
INDEX TERMS
similarity search, multidimensional data sequence, multimedia retrieval, video search, subsequence finding
CITATION
Seok-Lyong Lee, Seok-Ju Chun, Deok-Hwan Kim, Ju-Hong Lee, Chin-Wan Chung, "Similarity Search for Multidimensional Data Sequences", ICDE, 2000, 2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013 IEEE 29th International Conference on Data Engineering (ICDE) 2000, pp. 599, doi:10.1109/ICDE.2000.839473
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