The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
As transformações de dimensões inferiores em correspondência de sequências semelhantes mostram características de desempenho diferentes dependendo do tipo de dados de série temporal. Neste artigo propomos uma abordagem híbrida que explora múltiplas transformações ao mesmo tempo em um único índice híbrido. Essa abordagem híbrida tem vantagens de explorar o efeito semelhante do uso de múltiplas transformações e reduzir a sobrecarga de manutenção do índice. Para isso, propomos primeiro uma nova noção de transformação híbrida de dimensão inferior que extrai vários recursos usando diferentes transformações. A seguir definimos o distância híbrida para calcular a distância entre os pontos transformados híbridos. Provamos então formalmente que a abordagem híbrida realiza correspondência de sequências semelhantes corretamente. Apresentamos também a construção de índices e algoritmos de correspondência de sequências semelhantes baseados na transformação híbrida e distância. Os resultados experimentais mostram que a nossa abordagem híbrida supera a abordagem baseada em transformação única.
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Yang-Sae MOON, Jinho KIM, "Hybrid Lower-Dimensional Transformation for Similar Sequence Matching" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 3, pp. 541-544, March 2009, doi: 10.1587/transinf.E92.D.541.
Abstract: Lower-dimensional transformations in similar sequence matching show different performance characteristics depending on the type of time-series data. In this paper we propose a hybrid approach that exploits multiple transformations at a time in a single hybrid index. This hybrid approach has advantages of exploiting the similar effect of using multiple transformations and reducing the index maintenance overhead. For this, we first propose a new notion of hybrid lower-dimensional transformation that extracts various features using different transformations. We next define the hybrid distance to compute the distance between the hybrid transformed points. We then formally prove that the hybrid approach performs similar sequence matching correctly. We also present the index building and similar sequence matching algorithms based on the hybrid transformation and distance. Experimental results show that our hybrid approach outperforms the single transformation-based approach.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.541/_p
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@ARTICLE{e92-d_3_541,
author={Yang-Sae MOON, Jinho KIM, },
journal={IEICE TRANSACTIONS on Information},
title={Hybrid Lower-Dimensional Transformation for Similar Sequence Matching},
year={2009},
volume={E92-D},
number={3},
pages={541-544},
abstract={Lower-dimensional transformations in similar sequence matching show different performance characteristics depending on the type of time-series data. In this paper we propose a hybrid approach that exploits multiple transformations at a time in a single hybrid index. This hybrid approach has advantages of exploiting the similar effect of using multiple transformations and reducing the index maintenance overhead. For this, we first propose a new notion of hybrid lower-dimensional transformation that extracts various features using different transformations. We next define the hybrid distance to compute the distance between the hybrid transformed points. We then formally prove that the hybrid approach performs similar sequence matching correctly. We also present the index building and similar sequence matching algorithms based on the hybrid transformation and distance. Experimental results show that our hybrid approach outperforms the single transformation-based approach.},
keywords={},
doi={10.1587/transinf.E92.D.541},
ISSN={1745-1361},
month={March},}
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TY - JOUR
TI - Hybrid Lower-Dimensional Transformation for Similar Sequence Matching
T2 - IEICE TRANSACTIONS on Information
SP - 541
EP - 544
AU - Yang-Sae MOON
AU - Jinho KIM
PY - 2009
DO - 10.1587/transinf.E92.D.541
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2009
AB - Lower-dimensional transformations in similar sequence matching show different performance characteristics depending on the type of time-series data. In this paper we propose a hybrid approach that exploits multiple transformations at a time in a single hybrid index. This hybrid approach has advantages of exploiting the similar effect of using multiple transformations and reducing the index maintenance overhead. For this, we first propose a new notion of hybrid lower-dimensional transformation that extracts various features using different transformations. We next define the hybrid distance to compute the distance between the hybrid transformed points. We then formally prove that the hybrid approach performs similar sequence matching correctly. We also present the index building and similar sequence matching algorithms based on the hybrid transformation and distance. Experimental results show that our hybrid approach outperforms the single transformation-based approach.
ER -