• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.437-440
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• 2003

When we search database with a query image, the retrieval efficiency will vary from each kind of descriptor. Even the best representative descriptor, it results a few useless images that don't match with query image. This type of error can be reduced by adopting another descriptor which extracts features in different way. At present, the choice of descriptors is base on intuitive and experimental method. By theoretic accessing to the problem of descriptor choice, we can solve the given problem in the objective and rational way. In this study, we intend to make a composite of descriptors that can reduce retrieval error by adopting principal component analysis.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.487-490
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• 1999

In this paper we propose an efficient content-based image retrieval method using the color and wavelet based features. The color features are extracted from color histograms of the global image and the wavelet based features are extracted from the invariant moments of the high-pass band image through the spatial-frequency analysis of the wavelet transform. The proposed algorithm, called color and wavelet features based query(CWBQ), is composed of two-step query operations for efficient image retrieval: the coarse level filtering operation and the fine level matching operation. In the first filtering operation, the color histogram feature is used to filter out the dissimilar images quickly from a large image database. The second matching operation applies the wavelet based feature to the retained set of images to retrieve all relevant images successfully. The experimental results show that the proposed algorithm yields more improved retrieval accuracy with computationally efficiency than the previous methods.

• Journal of the Korean Society for Library and Information Science
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• v.30 no.1
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• pp.49-67
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• 1996

The previous study showed a large variation in performance within the queries and suggested that characteristics of queries cotribute to retrieval performance. Three attributes, specificity, complexity and recency were used to analyze the different results within queries. The result showed that subject searching retrieve more relevant documents for a Query with low specificity than a query with high specificity and that queries from the doctoral students' dissertations were specific queries with high specificity.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.635-637
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• 2012

In Gen-2 Q-algorithm, the value of initial $Q_{fp}$ and weight C is not defined in the standard. If we let the initial $Q_{fp}$ be large or small, the number of empty slot will be large during the initial query round or almost all the slots will be collided, respectively. Also, if the reader selects an inappropriate weight, there are a lot of empty or collided slots. As a result, it is anticipated that the performance will be declined because the frame size does not converge to the optimal point quickly during the query round. In this paper, we analyze how the performances of Gen-2 Q-algorithm will be affected by the weight and initial slot-count size.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.11a
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• pp.361-364
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• 2006

최근에 제한된 데이터 셋보다 센서 데이터 처리, 웹 서버 로그나 전화 기록과 같은 다양한 트랜잭션 로그 분석 등과 관련된 데이터 스트림 처리에 더 많은 관심이 집중되고 있으며, 특히 데이터 스트림의 질의 처리에 대한 관심이 증가하고 있다. 본 논문에서는 질의 중에서 2 개 이상의 스트림을 조인하는 조인 연속 질의를 처리하는 방법과 성능에 대해서 연구한다. 각 조인의 비용을 스트림의 입력 속도와 조인 선택도를 이용한 조인 비용 모델로 정의하고 그리디 알고리즘을 이용하여 최적화하는 기법을 제안하고 실험을 통해 다양한 스트림 환경에서 최적화 알고리즘이 어떤 성능을 보이는 지를 알아본다.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.729-732
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• 2019

데이터로그는 논리형 선언형 프로그래밍 언어로, 특히 재귀적인(recursion) 알고리즘을 표현하기 편리한 언어이다. 대표적인 데이터로그 시스템으로는 CORAL, LogicBlox, XSB, Soufflé가 있다. 본논문에서는 이 네 가지 시스템의 특징을 설명하고, 세 가지 벤치마크, 이행적 폐쇄(Transitive closure), 동세대(same generation), 포인터 분석(pointer analysis)으로 데이터로그 시스템들의 재귀 질의(recursive query) 처리 성능을 비교하였다.

• Proceedings of the Korea Society for Simulation Conference
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• 1992.10a
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• pp.12-12
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• 1992

This paper proposes an analysis method of Petri nets (PNs) using the relational algebra (RA). More specifically, we represent PNs in relations of the relational model. Based on such representation, we first develop an algorithms for analyzing properties of PNs, such as boundedness, conservation, coverability, reachability, and liveness. The advantage of this approach is as follows: First, the algorithms represented by RA can be easily converted to a query language such as SQL of the widely used, commercial relational database management systems (DBMSs). Second, we can alleviate the problem of state space explosion because relational DBMSs can handle large amounts of data efficiency. Finally, we can use the DBMS's query language to interpret the Petri nets and make simulation.

• Journal of the Korea Society for Simulation
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• v.1 no.1
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• pp.37-47
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• 1992

This paper proposes an analysis method of petri nets(PNs) using the relational algebra(RA). More wpecifically, we represent PNs in relations of the relational model. Based on such representation, we first develop an algorithm for generating reachability trees of PNs. we then develop an algorithm for generating reachability trees of PNs. We then develop algorithms for analyzing properties of PNs, such as boundedness, conservation, coverability, reachability, and liveness. The advantage of this approach is as follows: First, the algorithms represented by RA can be easity converted to a query language such as SQL of the widely used, commerical relational database management systems(DBMSs). Second, we can alleviate the problem of state space explosion because relational DBMSs can handle large amounts of data efficiently. Finally, we can use the DBMS's query language to interpret the Petri nets and make simulation.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.119-126
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• 2024

The volume of genomic data is constantly increasing in various modern industries and research fields. This growth presents new challenges and opportunities in terms of the quantity and diversity of genetic data. In this paper, we propose a distributed cloud system for integrating and managing large-scale gene databases. By introducing a distributed data storage and processing system based on the Hadoop Distributed File System (HDFS), various formats and sizes of genomic data can be efficiently integrated. Furthermore, by leveraging Spark on YARN, efficient management of distributed cloud computing tasks and optimal resource allocation are achieved. This establishes a foundation for the rapid processing and analysis of large-scale genomic data. Additionally, by utilizing BigQuery ML, machine learning models are developed to support genetic search and prediction, enabling researchers to more effectively utilize data. It is expected that this will contribute to driving innovative advancements in genetic research and applications.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06a
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• pp.5-6
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• 2007