• Proceedings of the Korea Information Processing Society Conference
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• 2004.11a
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• pp.31-34
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• 2004

서브시퀀스 매칭은 시계열 데이터베이스에서 질의 시퀀스와 유사한 서브시퀀스틀 찾아내는 연산이다. 기존의 서브시퀀스 매칭 알고리즘들은 하나의 인덱스만을 사용하여 검색을 수행하기 때문에, 인덱스를 생성하기 위하여 데이터 시퀀스로부터 추출한 윈도우의 크기와 질의 시퀀스의 길이 간의 차이가 커질수록 검색 성능이 급격히 저하되는 문제점을 갖고 있다. 본 논문에서는 이러한 기존 알고리즘의 문제점을 해결하기 위하여 인덱스 보간법에 기반한 새로운 서브시퀀스 매칭 기법을 제안한다. 인덱스 보간법이란 하나 이상의 인덱스를 구축하고 주어진 질의 시퀀스의 길이에 따라 적절한 인덱스를 선택하여 검색을 수행하는 기법이다. 본 논문에서는 서브시퀀스 매칭 비용 공식을 산출하고, 이 비용 공식에 기반하여 제안된 기법의 성능을 최적화 하도록 다수의 인덱스를 구성하는 알고리즘을 제시한다. 마지막으로, 실제 데이터를 이용한 여러 가지 실험을 통하여 제안된 기법의 우수성을 정량적으로 검증한다.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.344-356
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• 2001

This paper deals with the subsequence searching problem under time-warping in sequence databases. Our work is motivated by the observation that subsequence searches slow down quadratically as the average length of data sequences increases. To resolve this problem, the Segment-Based Approach for Subsequence Searches (SBSS) is proposed. The SBASS divides data and query sequences into a series of segments, and retrieves all data subsequences that satisfy the two conditions: (1) the number of segments is the same as the number of segments in a query sequence, and (2) the distance of every segment pair is less than or equal to a tolerance. Our segmentation scheme allows segments to have different lengths; thus we employ the time warping distance as a similarity measure for each segment pair. For efficient retrieval of similar subsequences, we extract feature vectors from all data segments exploiting their monotonically changing properties, and build a spatial index using feature vectors. Using this index, queries are processed with the four steps: (1) R-tree filtering, (2) feature filtering, (3) successor filtering, and (4) post-processing. The effectiveness of our approach is verified through extensive experiments.

• The KIPS Transactions:PartD
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• v.15D no.6
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• pp.777-784
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• 2008

Recently in the field of model checking, to overcome the state explosion problem, the method of using a SAT-solver is mainly researched. To use a SAT-solver, the system to be verified is translated into CNF and the Boolean cardinality constraint is widely used in translating the system into CNF. In BCC it is dealt with set of boolean variables, but there is no translating method of the sequence among Boolean variables. In this paper, we propose methods for translating the problem, which is extracting a subsequence with length k from a sequence of Boolean variables, into CNF formulas. Through experimental results, we show that our method is more efficient than using only BCC.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.31-33
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• 2004

대규모 DNA 시퀀스를 대상으로 하여 서브시퀀스를 고속으로 검색하기 위한 인덱싱 방법으로서 접미어 트리가 유용하다. 그러나 접미어 트리는 데이터 크기의 약 100배에 해당하는 방대한 저장 공간을 필요로 한파. 본 논문에서는 기존 접미어 트리의 검색 성능을 유지하며, 저장 공간을 획기적으로 감소시킬 수 있는 새로운 인덱스 구조를 제안한다. 제안된 인덱싱 방안에서는 DNA 시퀀스 내의 모든 염기 위치에 고정 길이의 슬라이딩 윈도우를 위치시켜, 윈도우 크기에 해당하는 연속된 서브시퀀스를 추출한 후, 이들을 대상으로 트라이를 구성한다. 트라이는 저장 공간 감소를 위하여 각 문자를 최소 비트 정보로 표현하며, 저장 구조로서 포인터를 사용하지 않는 디스크 기반의 이진 트라이 구조를 사용한다. DNA 서브시퀀스 검색을 효율적으로 처리하기 위한 인덱스 기반의 질의 처리 알고리즘을 제안하고 실험을 통하여 그 유용성을 보인다. 제안된 인덱스는 접미어 트리의 약 10분의 1의 저장 공간을 필요로 하며, 데이터 크기 증가에 거의 영향을 받지 않는 안정된 고속 검색 성능을 지원한다.

• Journal of KIISE:Databases
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• v.34 no.2
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• pp.119-132
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• 2007

In molecular biology, approximate subsequence search is one of the most important operations. In this paper, we propose an accurate and efficient method for approximate subsequence search in large DNA databases. The proposed method basically adopts a binary trie as its primary structure and stores all the window subsequences extracted from a DNA sequence. For approximate subsequence search, it traverses the binary trie in a breadth-first fashion and retrieves all the matched subsequences from the traversed path within the trie by a dynamic programming technique. However, the proposed method stores only window subsequences of the pre-determined length, and thus suffers from large post-processing time in case of long query sequences. To overcome this problem, we divide a query sequence into shorter pieces, perform searching for those subsequences, and then merge their results. To verify the superiority of the proposed method, we conducted performance evaluation via a series of experiments. The results reveal that the proposed method, which requires smaller storage space, achieves 4 to 17 times improvement in performance over the suffix tree based method. Even when the length of a query sequence is large, our method is more than an order of magnitude faster than the suffix tree based method and the Smith-Waterman algorithm.

• The KIPS Transactions:PartD
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• v.12D no.3 s.99
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• pp.345-354
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• 2005

Subsequence matching is one of the most important operations in the field of data mining. The existing subsequence matching algorithms use only one index, and their performance gets worse as the difference between the length of a query sequence and the site of windows, which are subsequences of a same length extracted from data sequences to construct the index, increases. In this paper, we propose a new subsequence matching method based on index interpolation to overcome such a problem. An index interpolation method constructs two or more indexes, and performs search ing by selecting the most appropriate index among them according to the given query sequence length. In this paper, we first examine the performance trend with the difference between the query sequence length and the window size through preliminary experiments, and formulate a search cost model that reflects the distribution of query sequence lengths in the view point of the physical database design. Next, we propose a new subsequence matching method based on the index interpolation to improve search performance. We also present an algorithm based on the search cost formula mentioned above to construct optimal indexes to get better search performance. Finally, we verify the superiority of the proposed method through a series of experiments using real and synthesized data sets.

• The KIPS Transactions:PartD
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• v.9D no.2
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• pp.173-184
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• 2002

This paper discuss an index-based subsequence matching that supports time warping in large sequence databases. Time warping enables finding sequences with similar patterns even when they are of different lengths. In earlier work, Kim et al. suggested an efficient method for whole matching under time warping. This method constructs a multidimensional index on a set of feature vectors, which are invariant to time warping, from data sequences. For filtering at feature space, it also applies a lower-bound function, which consistently underestimates the time warping distance as well as satisfies the triangular inequality. In this paper, we incorporate the prefix-querying approach based on sliding windows into the earlier approach. For indexing, we extract a feature vector from every subsequence inside a sliding window and construct a multidimensional index using a feature vector as indexing attributes. For query processing, we perform a series of index searches using the feature vectors of qualifying query prefixes. Our approach provides effective and scalable subsequence matching even with a large volume of a database. We also prove that our approach does not incur false dismissal. To verify the superiority of our approach, we perform extensive experiments. The results reveal that our approach achieves significant speedup with real-world S&P 500 stock data and with very large synthetic data.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.841-843
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• 2005

비디오 데이터 분석은 감시, 검색, 스포츠 경기 자동 요약 등 많은 분야에서 사용되는 기술이다. 그러나 감시 카메라나 스포츠 경기 비디오와 같이 사람의 영역이 저해상도인 환경에서는 포즈 추정, 모델과의 매칭이 어렵기 때문에 제스처 인식 연구는 많이 이루어지고 있지 못하다. 본 논문에서는 카메라가 Pan/Tilt/Zoom 동작을 하고 사람이 빠르게 움직이는 방송용 테니스 비디오에서, 사람을 추출하고, Curvature Scale Space를 기반으로 한 특징을 추출하여 학습된 포즈 모델과 매칭하는 방법과, 차원의 축소를 통해 일련의 포즈들을 학습된 제스처와 매칭하는 방법을 제안한다. 50개의 방송용 테니스 경기 비디오 장면에 대하여 서브 제스처 추출을 수행한 결과, 서브 포즈에 대하여 모델과 매칭이 잘 되고, 매칭이 되지 않는 포즈를 포함하는 시퀀스에 대해서도 강인한

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.39-47
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• 2024

Time series analysis is widely employed by many organizations to solve business problems, as it extracts various information and insights from chronologically ordered data. Among its applications, measuring time series similarity is a step to identify time series with similar patterns, which is very important in time series analysis applications such as time series search and clustering. In this study, we propose an efficient method for measuring time series similarity that focuses on anomalies rather than the entire series. In this regard, we validate the proposed method by measuring and analyzing the rank correlation between the similarity measure for the set of subsets extracted by anomaly detection and the similarity measure for the whole time series. Experimental results, especially with stock time series data and an anomaly proportion of 10%, demonstrate a Spearman's rank correlation coefficient of up to 0.9. In conclusion, the proposed method can significantly reduce computation cost of measuring time series similarity, while providing reliable time series search and clustering results.