• 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.

• The KIPS Transactions:PartD
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• v.10D no.3
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• pp.501-512
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• 2003

This paper proposes an indexing technique for fast retrieval of similar image subsequences using the multi-dimensional time warping distance. The time warping distance is a more suitable similarity measure than Lp distance in many applications where sequences may be of different lengths and/or different sampling rates. Our indexing scheme employs a disk-based suffix tree as an index structure and uses a lower-bound distance function to filter out dissimilar subsequences without false dismissals. It applies the normaliration for an easier control of relative weighting of feature dimensions and the discretization to compress the index tree. Experiments on medical and synthetic image sequences verify that the proposed method significantly outperforms the naive method and scales well in a large volume of image sequence databases.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.52-59
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• 2011

In this paper, we propose an indexing method to support efficient similarity search for sequence databases. We present a new distance measurement called minimum DTW distance to enhance the filtering effects. The minimum DTW distance is to measure the minimum distance between a sequence data and the group of similar sequences. It enables similarity search through hierarchical index structure by filtering sequence databases. Finally, we show the superiority of our method through some experiments.

• The KIPS Transactions:PartD
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• v.10D no.2
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• pp.283-292
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• 2003

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.

• The KIPS Transactions:PartD
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• v.12D no.3 s.99
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• pp.355-364
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• 2005

Previous researches on subsequence matching have been focused on how to make indexes in order to speed up the matching time, and do not take into account the effectiveness issues of subsequence matching methods. This paper considers the effectiveness of subsequence matching methods and proposes two metrics for effectiveness evaluations of subsequence matching algorithms. We have applied the proposed metrics to Korean stock data and five known matching algorithms. The analysis on the empirical data shows that two methods (i.e., the method supporting normalization, and the method supporting scaling and shifting) outperform the others in terms of the effectiveness of subsequence matching.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.760-762
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• 2008

음악에서는 다양한 감정의 표현을 시간에 따른 음악 무드의 전이로 표현한다. 본 연구에서는 Longest Common Subsequence (LCS) 알고리즘 및 k-Means 알고리즘에 기반한 유사 음악 검색 기법을 제안한다. 우선, 음악 무드의 흐름을 무드 세그먼트 단위로 나누고, 이를 추출된 다양한 음악 특성을 k-Means 알고리즘으로 분류하여 무드 시퀀스로 변환한다. 또한, 유사한 무드의 흐름을 가지는 음악을 검색하기 위해 LCS 알고리즘에 기반한 무드 시퀀스의 유사도를 정의한다. 본 논문은 제안된 내용을 바탕으로 실험과 설문 조사를 통해, 기존의 전역적 특성 검색 방식보다 시퀀스를 이용한 검색방식이 좀 더 효율적임을 증명하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.1068-1069
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• 2012

시계열 데이타는 일정 시간 간격으로 측정한 값의 시퀀스를 뜻하는데, 사용자에 의해 주어진 질의 시퀀스와 유사한 데이타 시퀀스를 검색하는 방법을 유사 시퀀스 매칭이라고 한다. 본 논문에서는 유사 시퀀스 매칭 시, 질의 시퀀스로 MBR을 구성할 때 한 개의 MBR이 아닌 다수의 MBR로 구성하는 방법을 제안하였다. 다수의 MBR로 구성하여 질의 처리를 하면 질의 시퀀스의 길이가 길 경우 적은 비용으로 질의 처리를 수행할 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05c
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• pp.1583-1586
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• 2003

DNA 시퀀스는 A, C, G, T 네 개의 문자로 구성된 매우 긴 시퀀스로 볼 수 있다. 고속으로 유사 DNA 시퀀스를 검색하기 위하여 인덱싱 기술을 이용하는 것이 일반적이다. 그러나 검색 대상의 유전체 데이터베이스는 그 크기가 매우 크며, 또한 지수 함수적으로 크기가 급속히 증가하고 있으므로, 기존의 인덱싱 기법을 그대로 적용할 경우, 실용성에 한계가 있다. 본 논문에서는 이와 같은 문제점을 해결할 수 있는 대규모 유전체 데이터베이스를 위한 효율적인 인덱싱 기법과 질의처리 기법을 제안한다. 기본 구조로서 접미어 트라이를 사용하며, 접미어 트리 인덱스 구조의 최대 단점인 인덱스 크기를 줄일 수 있는 데이터 압축 표현 방식을 제안한다. 또한 제안된 데이터 압축 표현 방식의 디스크 기반 인덱스 구성 알고리즘과 이를 활용한 부분 시퀀스 검색 알고리즘을 보이고, 그 저장 성능의 비교 평가결과를 보인다.

• Journal of KIISE:Databases
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• v.31 no.4
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• pp.395-408
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• 2004

A conjunctive Boolean text query refers to a query that searches for tort documents containing all of the specified keywords, and is the most frequently used query form in information retrieval systems. Typically, the query specifies a long list of keywords for better precision, and in this case, the order of keyword processing has a significant impact on the query speed. Currently known approaches to this ordering are based on heuristics and, therefore, cannot guarantee an optimal ordering. We can use a systematic approach by leveraging a database query processing algorithm like the dynamic programming, but it is not suitable for a text query with a typically long list of keywords because of the algorithm's exponential run-time (Ο(n2$^{n-1}$)) for n keywords. Considering these problems, we propose a new approach based on a property called the join sequence separability. This property states that the optimal join sequence is separable into two subsequences of different join methods under a certain condition on the joined relations, and this property enables us to find a globally optimal join sequence in Ο(n2$^{n-1}$). In this paper we describe the property formally, present an optimization algorithm based on the property, prove that the algorithm finds an optimal join sequence, and validate our approach through simulation using an analytic cost model. Comparison with the heuristic text query optimization approaches shows a maximum of 100 times faster query processing, and comparison with the dynamic programming approach shows exponentially faster query optimization (e.g., 600 times for a 10-keyword query).

• Journal of Korea Multimedia Society
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• v.12 no.4
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• pp.492-501
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• 2009

Information on movement of objects in videos can be used as an important part in categorizing and separating the contents of a scene. This paper is proposing a shape-based movement-matching algorithm to effectively find the movement of an object in video streams. Information on object movement is extracted from the object boundaries from the input video frames becoming expressed in continuous 2D shape information while individual 2D shape information is converted into a lD shape feature using the shape descriptor. Object movement in video can be found as simply as searching for a word in a text without a separate movement segmentation process using the sequence of the shape descriptor listed according to order. The performance comparison results with the MPEG-7 shape variation descriptor showed that the proposed method can effectively express the movement information of the object and can be applied to movement search and analysis applications.