• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.1079-1080
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• 2012

시계열 데이터는 경제, 기상, 의료 등 다양한 분야에서 사용되고 있으며, 시계열 데이터 상에서의 검색 방법에 대한 관심이 더욱 높아지고 있다. 시계열 데이터는 각 시간별로 측정한 실수 값의 시퀀스로, 사용자가 원하는 질의 시쿠퀀스와 유사한 데이터 시퀀스를 찾는 방법인 유사 시퀀스 매칭 방법을 조사한다. 유사 시퀀스 매칭 방법은 전체 매칭과 서브시퀀스 매칭으로 분류되며, 서브시퀀스 매칭의 대표적인 방법으로 전체매칭을 일반화한 방법인 FRM, FRM의 윈도우 구성 방법에 대해 이원적으로 접근한 DualMatch, FRM과 DualMatch를 일반화한 GeneralMatch가 있으며, 각 방법에 대한 비교분석을 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.121-124
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• 2005

본 논문에서는 타임 워핑 하의 시계열 서브시퀀스 매칭을 처리하는 방법에 대하여 논의한다. 타임 워핑은 시퀀스의 길이가 서로 다른 경우에도 유사한 패턴을 갖는 시퀀스들을 찾을 수 있도록 해 주는 변환이다. 접두어 질의 기법(prefix-querying method)는 착오 기각(false dismissal) 없이 타임 워핑 하의 시계열 서브시퀀스 매칭을 처리하는 인덱스를 이용한 최초의 방식이다. 이 방법은 사용자가 질의를 편리하게 작성하도록 하기 위하여 기본 거리 함수로서 $L_{\infty}$를 사용한다. 본 논문에서는 $L_{\infty}$ 대신 타임 워핑 하의 시계열 서브시퀀스 매칭에서 기본 거리 함수로서 가장 널리 사용되는 $L_1$을 적용할 수 있도록 접두어 질의를 확장한다. 또한, 제안된 기법으로 타임 워핑 하의 시계열 서브시퀀스 매칭을 수행하는 경우 착오 기각이 발생하지 않음을 이론적으로 증명한다. 다양한 실험을 통한 성능 평가를 통하여 본 연구에서 제시하는 기법의 우수성을 검증한다. 실험 결과에 의하면, 제안된 기법은 가장 좋은 성능을 보이는 기존의 기법과 비교하여 매우 뛰어난 성능 개선 효과를 보이는 것으로 나타났다.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1003-1010
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• 2004

In this paper, we address efficient processing of subsequence matching in time-series databases. We first point out the performance problems occurring in the index searching of a prior method for subsequence matching. Then, we propose a new method that resolves these problems. Our method starts with viewing the index searching of subsequence matching from a new angle, thereby regarding it as a kind of a spatial-join called a window-join. For speeding up the window-join, our method builds an R＊-tree in main memory for f query sequence at starting of sub-sequence matching. Our method also includes a novel algorithm for joining effectively one R＊-tree in disk, which is for data sequences, and another R＊-tree in main memory, which is for a query sequence. This algorithm accesses each R＊-tree page built on data sequences exactly cure without incurring any index-level false alarms. Therefore, in terms of the number of disk accesses, the proposed algorithm proves to be optimal. Also, performance evaluation through extensive experiments shows the superiority of our method quantitatively.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.2
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• pp.43-46
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• 2014

A time-series data is the collection of real numbers over the time intervals. One of the main tasks in time-series data is efficiently to find subsequences similar to a given query sequence. In this paper, we propose an efficient subsequence matching method, which is called Instance-Match (I-Match). I-Match constructs a virtual window in order to reduce false alarms. Through the experiment with real data set and query sets, we show that I-Match improves query processing time by up to 2.95 times and significantly reduces the number of candidates comparing to Dual Match.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10a
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• pp.39-42
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• 2001

본 논문에서는 시계열 데이터베이스에서 서브시퀀스 매칭을 효과적으로 처리하는 방안에 관하여 논의한다. 먼저, 서브시퀀스 매칭의 후처리 과정에서 발생하는 기존 기법의 문제점을 지적하고, 이를 해결할 수 있는 최적의 기법을 제안하였다. 제안된 기법은 이진 트리 내에 후보 시퀀스에 대한 정보를 삽입해 둠으로써 같은 시퀀스에 속하는 후보 윈도우들과 같은 서브시퀀스에 속하는 후보 윈도우들을 연속적으로 처리하는 방식을 사용한다. 이 결과, 디스크 액세스와 서브시퀀스 비교의 측면에서 중복 작업을 완전히 제거할 수 있다. 제안된 기법의 성능 개선 효과를 검증하기 위하여 실제 주식 데이터를 위한 성능 평가를 수행하였다. 실험 결과에 의하면, 제안된 기법은 기존의 기법과 비교하여 전체적으로 55배에서 156배까지의 성능 개선 효과가 있는 것으로 나타났다.

• The KIPS Transactions:PartD
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• v.9D no.4
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• pp.555-560
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• 2002

Subsequence matching, which consists of index searching and post-processing steps, is an operation that finds those subsequences whose changing patterns are similar to that of a given query sequence from a time-series database. This paper discusses optimization of post-processing for subsequence matching. The common problem occurred in post-processing of previous methods is to compare the candidate subsequence with the query sequence for discarding false alarms whenever each candidate subsequence appears during index searching. This makes a sequence containing candidate subsequences to be accessed multiple times from disk, and also have a candidate subsequence to be compared with the query sequence multiple times. These redundancies cause the performance of subsequence matching to degrade seriously. In this paper, we propose a new optimal method for resolving the problem. The proposed method stores ail the candidate subsequences returned by index searching into a binary search tree, and performs post-processing in a batch fashion after finishing the index searching. By this method, we are able to completely eliminate the redundancies mentioned above. For verifying the performance improvement effect of the proposed method, we perform extensive experiments using a real-life stock data set. The results reveal that the proposed method achieves 55 times to 156 times speedup over the previous methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.117-120
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• 2004

본 논문에서는 시계열 데이터베이스에서 타임 워핑 하의 서브시퀀스 매칭을 효과적으로 처리하는 방안에 관하여 논의한다. 타임 워핑은 데이터베이스내 시퀀스들의 길이가 서로 다른 경우에도 유사한 패턴을 갖는 시퀀스들을 찾을 수 있도록 해 준다. 본 논문에서는 타임 워핑 하의 서브시퀀스 매칭을 위한 기존의 기본 처리 방식인 Naive-Scan의 CPU 처리 과정을 최적화하는 새로운 기법을 제안한다. 제안된 기법은 질의 시퀀스와 서브시퀀스들 간의 타임 워핑 거리들을 계산하는 과정에서 발생하는 중복 작업들을 사전에 제거함으로써 CPU 처리 성능을 극대화한다. 제안된 기법이 착오 기각을 발생시키지 않음과 Naive-Scan을 처리하기 위한 최적의 기법임을 이론적으로 규명한다. 또한, 다양한 실험을 통한 성능 평가에 의하여 제안된 최적화 기법이 가져오는 성능 개선 효과를 정량적으로 검증한다. 아울러, 제안된 기법이 기존의 여과 단계를 포함하는 방식인 LB-Scan과 ST-Filter의 후처리 단계에도 성공적으로 적용될 수 있음을 보인다.

• The KIPS Transactions:PartD
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• v.13D no.3 s.106
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• pp.357-368
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• 2006

This paper discusses the way of processing time-series subsequence matching under time warping. Time warping enables finding sequences with similar patterns even when they are of different lengths. The prefix-querying method is the first index-based approach that performs time-series subsequence matching under time warping without false dismissals. This method employs the $L_{\infty}$ as a base distance function for allowing users to issue queries conveniently. In this paper, we extend the prefix-querying method for absorbing $L_1$, which is the most-widely used as a base distance function in time-series subsequence matching under time warping, instead of $L_{\infty}$. We also formally prove that the proposed method does not incur any false dismissals in the subsequence matching. To show the superiority of our method, we conduct performance evaluation via a variety of experiments. The results reveal that our method achieves significant performance improvement in orders of magnitude compared with previous methods.

• Journal of KIISE:Databases
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• v.30 no.4
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• pp.381-396
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• 2003

Subsequence matching is an operation that finds subsequences whose changing patterns are similar to a given query sequence from time-series databases. This paper points out the performance bottleneck in subsequence matching, and then proposes an effective method that improves the performance of entire subsequence matching significantly by resolving the performance bottleneck. First, we analyze the disk access and CPU processing times required during the index searching and post processing steps through preliminary experiments. Based on their results, we show that the post processing step is the main performance bottleneck in subsequence matching, and them claim that its optimization is a crucial issue overlooked in previous approaches. In order to resolve the performance bottleneck, we propose a simple but quite effective method that processes the post processing step in the optimal way. By rearranging the order of candidate subsequences to be compared with a query sequence, our method completely eliminates the redundancy of disk accesses and CPU processing occurred in the post processing step. We formally prove that our method is optimal and also does not incur any false dismissal. We show the effectiveness of our method by extensive experiments. The results show that our method achieves significant speed-up in the post processing step 3.91 to 9.42 times when using a data set of real-world stock sequences and 4.97 to 5.61 times when using data sets of a large volume of synthetic sequences. Also, the results show that our method reduces the weight of the post processing step in entire subsequence matching from about 90% to less than 70%. This implies that our method successfully resolves th performance bottleneck in subsequence matching. As a result, our method provides excellent performance in entire subsequence matching. The experimental results reveal that it is 3.05 to 5.60 times faster when using a data set of real-world stock sequences and 3.68 to 4.21 times faster when using data sets of a large volume of synthetic sequences compared with the previous one.

• Journal of KIISE:Databases
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• v.34 no.6
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• pp.503-517
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• 2007

In this paper we address the MBR-safe property of Piecewise Aggregation Approximation(PAA), and propose an of efficient subsequence matching method based on the MBR-safe PAA. A transformation is said to be MBR-safe if a low-dimensional MBR to which a high- dimensional MBR is transformed by the transformation contains every individual low-dimensional sequence to which a high-dimensional sequence is transformed. Using an MBR-safe transformation we can reduce the number of lower-dimensional transformations required in similar sequence matching, since it transforms a high-dimensional MBR itself to a low-dimensional MBR directly. Furthermore, PAA is known as an excellent lower-dimensional transformation single its computation is very simple, and its performance is superior to other transformations. Thus, to integrate these advantages of PAA and MBR-safeness, we first formally confirm the MBR-safe property of PAA, and then improve subsequence matching performance using the MBR-safe PAA. Contributions of the paper can be summarized as follows. First, we propose a PAA-based MBR-safe transformation, called mbrPAA, and formally prove the MBR-safeness of mbrPAA. Second, we propose an mbrPAA-based subsequence matching method, and formally prove its correctness of the proposed method. Third, we present the notion of entry reuse property, and by using the property, we propose an efficient method of constructing high-dimensional MBRs in subsequence matching. Fourth, we show the superiority of mbrPAA through extensive experiments. Experimental results show that, compared with the previous approach, our mbrPAA is 24.2 times faster in the low-dimensional MBR construction and improves subsequence matching performance by up to 65.9%.