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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 시계열 데이터베이스에서 임의 순서의 이동 평균 변환을 지원하는 하위 시퀀스 매칭 알고리즘을 제안합니다. 이동 평균 변환은 잡음의 영향을 줄여주며 전반적인 추세를 찾는 데 유용하므로 계량경제학 등 다양한 분야에서 사용되어 왔습니다. 제안된 알고리즘은 Faloutsos et al.이 제안한 기존 하위 시퀀스 매칭 알고리즘을 확장합니다. (줄여서 SUB94). 확장 없이 알고리즘을 적용하면 각 이동 평균 주문에 대한 인덱스를 생성해야 하며 심각한 저장 공간 및 CPU 시간 오버헤드가 발생합니다. 본 논문에서는 인덱스 보간(index interpolation) 개념을 사용하여 문제를 해결합니다. 인덱스 보간 선택된 몇 가지 사례에 대해 생성된 하나 이상의 색인을 사용하여 일부 기준을 만족하는 모든 사례를 검색하는 검색 방법으로 정의됩니다. 인덱스 보간에 기반한 제안 알고리즘은 미리 선택된 이동평균 차수에 대해 하나의 인덱스만을 사용할 수 있다. k 임의의 순서에 대해 하위 시퀀스 일치를 수행합니다. m (
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Woong-Kee LOH, Sang-Wook KIM, Kyu-Young WHANG, "Index Interpolation: A Subsequence Matching Algorithm Supporting Moving Average Transform of Arbitrary Order in Time-Series Databases" in IEICE TRANSACTIONS on Information,
vol. E84-D, no. 1, pp. 76-86, January 2001, doi: .
Abstract: In this paper we propose a subsequence matching algorithm that supports moving average transform of arbitrary order in time-series databases. Moving average transform reduces the effect of noise and has been used in many areas such as econometrics since it is useful in finding the overall trends. The proposed algorithm extends the existing subsequence matching algorithm proposed by Faloutsos et al. (SUB94 in short). If we applied the algorithm without any extension, we would have to generate an index for each moving average order and would have serious storage and CPU time overhead. In this paper we tackle the problem using the notion of index interpolation. Index interpolation is defined as a searching method that uses one or more indexes generated for a few selected cases and performs searching for all the cases satisfying some criteria. The proposed algorithm, which is based on index interpolation, can use only one index for a pre-selected moving average order k and performs subsequence matching for arbitrary order m (
URL: https://global.ieice.org/en_transactions/information/10.1587/e84-d_1_76/_p
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@ARTICLE{e84-d_1_76,
author={Woong-Kee LOH, Sang-Wook KIM, Kyu-Young WHANG, },
journal={IEICE TRANSACTIONS on Information},
title={Index Interpolation: A Subsequence Matching Algorithm Supporting Moving Average Transform of Arbitrary Order in Time-Series Databases},
year={2001},
volume={E84-D},
number={1},
pages={76-86},
abstract={In this paper we propose a subsequence matching algorithm that supports moving average transform of arbitrary order in time-series databases. Moving average transform reduces the effect of noise and has been used in many areas such as econometrics since it is useful in finding the overall trends. The proposed algorithm extends the existing subsequence matching algorithm proposed by Faloutsos et al. (SUB94 in short). If we applied the algorithm without any extension, we would have to generate an index for each moving average order and would have serious storage and CPU time overhead. In this paper we tackle the problem using the notion of index interpolation. Index interpolation is defined as a searching method that uses one or more indexes generated for a few selected cases and performs searching for all the cases satisfying some criteria. The proposed algorithm, which is based on index interpolation, can use only one index for a pre-selected moving average order k and performs subsequence matching for arbitrary order m (
keywords={},
doi={},
ISSN={},
month={January},}
부
TY - JOUR
TI - Index Interpolation: A Subsequence Matching Algorithm Supporting Moving Average Transform of Arbitrary Order in Time-Series Databases
T2 - IEICE TRANSACTIONS on Information
SP - 76
EP - 86
AU - Woong-Kee LOH
AU - Sang-Wook KIM
AU - Kyu-Young WHANG
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E84-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2001
AB - In this paper we propose a subsequence matching algorithm that supports moving average transform of arbitrary order in time-series databases. Moving average transform reduces the effect of noise and has been used in many areas such as econometrics since it is useful in finding the overall trends. The proposed algorithm extends the existing subsequence matching algorithm proposed by Faloutsos et al. (SUB94 in short). If we applied the algorithm without any extension, we would have to generate an index for each moving average order and would have serious storage and CPU time overhead. In this paper we tackle the problem using the notion of index interpolation. Index interpolation is defined as a searching method that uses one or more indexes generated for a few selected cases and performs searching for all the cases satisfying some criteria. The proposed algorithm, which is based on index interpolation, can use only one index for a pre-selected moving average order k and performs subsequence matching for arbitrary order m (
ER -