• Journal of Internet Computing and Services
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• v.14 no.2
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• pp.1-13
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• 2013

Recent advance and popularization of smart devices and web application services based on cloud computing have made end-users to directly produce and, at the same time, consume the image contents. This leads to demands of unified contents management services. Thus, this paper proposestag clustering method based on semantic similarity for effective image categorization. We calculate the cost of semantic similarity between tags and cluster tags that are closely related. If tags are in a cluster, we suppose that images with them are also in a same cluster. Furthermore, we could recommend tags for new images on the basis of initial clusters.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04a
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• pp.642-644
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• 2000

클러스터 시스템의 성능을 향상시키기 위해서는 Myrinet과 같은 고성능 통신망 인터페이스가 필수적이다. 그러나 Myrinet에서 동작하는 저수준 통신 계층들은 각기 고유한 기작을 사용하므로 호환성이 떨어진다. 따라서 MPI와 같은 통신 프로그래밍 표준을 효율적으로 구현하여 응용프로그램 수준에서 고성능과 호환성을 동시에 제공하여야 한다. 본 논문에서는 VMMC 통신 계층을 기반으로, 늦은 위치 갱신, 선택적 무복사 전송 등의 최적화 기법을 적용하여 우수한 성능의 MPI를 구현하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10c
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• pp.874-876
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• 2001

클러스터링(clustering)은 병렬 처리를 위한 기술로 비교적 값이 싼 컴퓨터들을 네트웍으로 연결하여 전체가 하나의 고성능 수퍼 컴퓨터처럼 동작하게 하는 기술이다. 이 클러스터 시스템의 성능을 최대한 활용하기 위해서는 디스크 입출력에 생기는 병목현상을 해결하여야 하는데, 그 해결책의 하나로 병렬파일시스템을 들 수 있다. 기존의 병렬파일시스템은 TCP/IP기반의 소켓으로 메시지를 주고받았다 그러나 TCP/IP는 프로토콜 오버헤드가 크고 처리 속도가 느리다. 본 논문에서는 이런 오버헤드를 줄이기 위해 도입된 Lightweight 메시징 기법인 VIA(Virtual Interface Architecture)를 이용하여 병렬파일시스템을 구현하기 위한 구체적인 방안을 제시하고 있다.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.5
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• pp.60-70
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• 2018

A software defined storage (SDS) is being deployed in cloud environment to allow multiple users to virtualize physical servers, but a solution for optimizing space efficiency with limited physical resources is needed. In the conventional data deduplication system, it is difficult to deduplicate redundant data uploaded to distributed storages. In this paper, we propose a distributed deduplication method using similarity-based clustering and multi-layer bloom filter. Rabin hash is applied to determine the degree of similarity between virtual machine servers and cluster similar virtual machines. Therefore, it improves the performance compared to deduplication efficiency for individual storage nodes. In addition, a multi-layer bloom filter incorporated into the deduplication process to shorten processing time by reducing the number of the false positives. Experimental results show that the proposed method improves the deduplication ratio by 9% compared to deduplication method using IP address based clusters without any difference in processing time.

• Journal of Digital Contents Society
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• v.18 no.5
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• pp.811-820
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• 2017

For content-based audio retrieval which is one of main functions in audio service, the techniques for extracting fingerprints from the audio source, storing and indexing them in a database are widely used. However, if the fingerprints of new audio sources are continually inserted into the database, there is a problem that space efficiency as well as audio retrieval performance are gradually deteriorated. Therefore, there is a need for techniques to support efficient expansion of audio database without periodic reorganization of the database that would increase the system operation cost. In this paper, we design a content-based audio retrieval system that solves this problem by using MapReduce and NoSQL database in a cluster computing environment based on the Shazam's fingerprinting algorithm, and evaluate its performance through a detailed set of experiments using real world audio data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.229-234
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• 2014

As association between sensor networks that collect environmental information by using numberous sensor nodes and smartphones that are equipped with various sensors, many applications understanding users' context have been developed to interact users and their environments. Collected data should be stored with XML formatted metadata containing semantic information to share the collected data. In case of distance based clustering schemes, the efficiency of data collection decreases because metadata files are extended and changed as the purpose of each system developer. In this paper, we proposed a clustering scheme considering the structural similarity of metadata to reduce clustering construction time and improve the similarity of metadata among member nodes in a cluster.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.6
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• pp.712-720
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• 2001

In this paper, we propose an efficient method to detect abrupt shot changes in a video sequence using an unsupervised clustering. Conventional clustering-based shot change detection algorithms use multiple features in order to overcome the shortcomings of a single feature. In such methods it is very important to determine the appropriate initial cluster centers well. In this paper we propose a modified k-means clustering algorithm which estimates the initial cluster center adaptively. Experimental results show that the proposed algorithm works well.

• Journal of the Korea Society of Computer and Information
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• v.13 no.2
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• pp.167-175
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• 2008

The current pre-distribution of secret keys uses a-composite random key and it randomly allocates keys. But there exists high probability not to be public-key among nodes and it is not efficient to find public-key because of the problem for time and energy consumption. We presents key establishment scheme designed to satisfy authentication and confidentiality, without the need of a key distribution center. Proposed scheme is scalable since every node only needs to hold a small number of keys independent of the network size, and it is resilient against node capture and replication due to the fact that keys are localized. In simulation result, we estimate process time of parameter used in proposed scheme and efficiency of Proposed scheme even if increase ECC key length.

• The KIPS Transactions:PartD
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• v.10D no.3
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• pp.459-470
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• 2003

It is required to research on high-dimensional index structures for efficiently retrieving high-dimensional data because an attribute vector in data warehousing and a feature vector in multimedia database have a characteristic of high-dimensional data. For this, many high-dimensional index structures have been proposed, but they have so called ‘dimensional curse’ problem that retrieval performance is extremely decreased as the dimensionality is increased. To solve the problem, the cell-based filtering (CBF) scheme has been proposed. But the CBF scheme show a linear decreasing on performance as the dimensionality. To cope with the problem, it is necessary to make use of parallel processing techniques. In this paper, we propose a parallel CBF scheme which uses a horizontally-partitioned technique as declustering. In order to maximize the retrieval performance of the proposed parallel CBF scheme, we construct our parallel CBF scheme under a SN (Shared Nothing) cluster architecture. In addition, we present a data insertion algorithm, a rage query processing one, and a k-NN query processing one which are suitable for the SN cluster architecture. Finally, we show that our parallel CBF scheme achieves better retrieval performance in proportion to the number of servers in the SN cluster architecture, compared with the conventional CBF scheme.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1642_1643
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• 2009

일반적으로, 퍼지 예측 시스템의 성능은 데이터의 특성과 퍼지 집합을 생성하기 위한 클러스터일 기법에 매우 의존적이다. 하지만, 예측을 위한 시계열 데이터들은 자연현상에 기인하는 강한 비선형적 특성을 가지고 있으므로 적합한 시스템을 구현하는 것에 많은 제약이 따른다. 따라서 본 논문에서는 시계열의 비선형적 특성을 적절히 취급하기 위하여, 그들로부터 생성 가능한 차분 데이터 중, 유효한 차분데이터를 이용하여 다중 모델 퍼지 예측 시스템을 구현함으로써, 보다 우수한 예측이 가능하도록 하였으며, 퍼지 시스템의 모델링에는 교차 상관분석기법에 따른 계층적 구조의 클러스터링 기법 (Hierarchical Cross-correlation and K-means Clustering Algorithms: HCKA)을 적용하여, 시스템을 위한 규칙기반의 적합성을 높일 수 있도록 하였다.