• 한국공간정보시스템학회:학술대회논문집
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• 2002.03a
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• pp.15-21
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• 2002

기존의 WebGIS는 구현방법에 따라서 서버중심과 클라이언트중심의 WebGIS모델로 구분할 수 있다. 서버중심의 WebGIS모델은 결과데이터가 이미지(JPG,GIF)로 클라이언트에 전송되므로 사용자는 기본적인 GIS 기능만을 처리할 수 있고, 데이터 확대 시 데이터가 손상되는 문제들이 있다. 클라이언트 중심의 WebGIS모델은 벡터데이터(Java-applet, ActiveCGM) 전송을 위해 고가의 특정 소프트웨어를 구입해야 하고, 플랫폼에 의존적이며, 표준화된 포맷이 아니므로 웹에서 데이터 상호 전송 및 호환의 문제가 있다. 본 논문에서는 기존의 WebGIS모델의 전반적인 분석을 통해서 각기 구축된 WebGIS의 구현방법과 문제점에 대해 고찰해 본다. 그리고 이런 문제점들이 대안인 XML(extensible Markup Language)으로 기술된 GML (Geographic Markup Language)과 SVG(Scalable Vector Graphics)를 이용한 WebGIS에 대한 연구와 향후 우리가 연구해야 할 방향에 대해 모색해 본다.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.354-359
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• 2008

Clustering is a method to classify the given data set with same property into several classes. To cluster data, many methods such as K-Means, Fuzzy C-Means(FCM), Mountain Method(MM), and etc, have been proposed and used. But the clustering results of conventional methods are sensitively influenced by initial values given for clustering in each method. Especially, FCM is very sensitive to noisy data, and cluster center distortion phenomenon is occurred because the method dose clustering through minimization of within-clusters variance. In this paper, we propose a clustering method which reduces cluster center distortion through merging the nearest data based on the data weight, and not being influenced by initial values. We show the effectiveness of the proposed through experimental results applied it to various types of data sets, and comparison of cluster centers with those of FCM.

• Journal of KIISE:Databases
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• v.37 no.6
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• pp.315-323
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• 2010

In Wireless Sensor Networks (WSNs), various Data-Centric Storages (DCS) schemes have been proposed to store the collected data and to efficiently process a query. A DCS scheme assigns distributed data regions to sensor nodes and stores the collected data to the sensor which is responsible for the data region to process the query efficiently. However, since the whole data stored in a node will be lost when a fault of the node occurs, the accuracy of the query processing becomes low, In this paper, we propose an in-network aggregation query processing method that assures the high accuracy of query result in the case of data loss due to the faults of the nodes in the DCS scheme. When a data loss occurs, the proposed method creates a compensation model for an area of data loss using the linear regression technique and returns the result of the query including the virtual data. It guarantees the query result with high accuracy in spite of the faults of the nodes, To show the superiority of our proposed method, we compare E-KDDCS (KDDCS with the proposed method) with existing DCS schemes without the data-loss correction method. In the result, our proposed method increases accuracy and reduces query processing costs over the existing schemes.

• Journal of Korean Library and Information Science Society
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• v.54 no.4
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• pp.393-414
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• 2023

The goal of this study is to investigate domestic research trend on research data study. To achieve this goal, articles related research data topic were collected from RISS. After data cleansing, 134 author keywords were extracted from a total of 58 articles and keyword network analysis was performed. As a result, first, the number of studies related to research data in Korea is still only 58, so it was found that many related studies need to be conducted in the future. Second, most research fields related to research data were focused on library and information science among complex studies. Third, as a result of frequency analysis of author keywords related to research data, 'research data management', 'research data sharing', 'data repository', and 'open science' were analyzed as major frequent keywords, so research data-related research focuses on the above keywords. The keyword network analysis results also showed that high-frequency keywords occupy a central position in degree centrality and betweenness centrality and are located as core keywords in related studies. Through the results of this study, we were able to identify trends related to recent research data and identify areas that require intensive research in the future.

• Journal of Korean Library and Information Science Society
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• v.49 no.3
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• pp.407-428
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• 2018

Metadata standard is very important element to describe a digital resource in the records lifecycle. Metadata standard is influenced by the purpose and content of tasks that are performed to a resource. But it does not reflect a task-centric viewpoint to cover the whole records-lifecycle because metadata is defined and designed from a resource-centric viewpoint. In other words, to cover the whole records-lifecycle using metadata, it is necessary to properly select and combine metadata property from a task-centric viewpoint. Therefore, we proposed a task-centric metadata application profile that can combine and select appropriate metadata properties according to requirements and tasks performed on resource.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.3-7
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• 2009

Data centric storages for sensor networks have been proposed to efficiently process multi-dimensional range queries as well as exact matches. Usually, a sensor network does not process only one type of the query but supports various types of queries such as range queries, exact matches and skyline queries. Therefore, a sensor network based on a data centric storage for range queries and exact matches should process skyline queries efficiently. However, existing algorithms for skyline queries have not considered the features of data centric storages. Some of the data centric storages store similar data in sensor nodes that are placed on geographically similar locations. Consequently, all data are ordered in a sensor network. In this paper, we propose a new skyline query processing algorithm that exploits the above features of data centric storages.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.661-666
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• 2003

In this paper, the conventional k-modes and fuzzy k-modes algorithms for clustering categorical data is extended by representing the clusters of categorical data with fuzzy centroids instead of the hard-type centroids used in the original algorithm. The hard-type centroids of the traditional algorithms had difficulties in dealing with ambiguous boundary data, which might be misclassified and lead to thelocal optima. Use of fuzzy centroids makes it possible to fully exploit the power of fuzzy sets in representing the uncertainty in the classification of categorical data. The distance measure between data and fuzzy centroids is more precise and effective than those of the k-modes and fuzzy k-modes. To test the proposed approach, the proposed algorithm and two conventional algorithms were used to cluster three categorical data sets. The proposed method was found to give markedly better clustering results.

• The Journal of the Acoustical Society of Korea
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• v.41 no.2
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• pp.199-209
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• 2022

Typical anomaly detection algorithms are trained by using prior data. Thus the batch learning based algorithms cause inevitable performance degradation when characteristics of newly incoming normal data change over time. We propose an online anomaly detection algorithm which can consider the gradual characteristic changes of incoming normal data. The proposed algorithm based on one-class classification model includes both offline and online learning procedures. In offline learning procedure, the algorithm learns the prior data to be close to centroid of the latent space and then updates the centroid of the latent space incrementally by new incoming data. In the online learning, the algorithm continues learning by using the updated centroid. Through experiments using public underwater acoustic data, the proposed online anomaly detection algorithm takes only approximately 2 % additional learning time for the incremental centroid update and learning. Nevertheless, the proposed algorithm shows 19.10 % improvement in Area Under the receiver operating characteristic Curve (AUC) performance compared to the offline learning model when new incoming normal data comes.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.235-238
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• 2003

Fuzzy C-Means(FCM) 알고리즘은 초기 군집 중심의 개수와 위치에 따라 군집 결과의 성능차이가 많이 나타난다. 하지만 일반적인 경우에 군집 중심의 개수는 분석가의 주관에 의해 결정되고, 임의적으로 결정되기 때문에 원래 데이터의 구조와는 무관하게 수행되어 최적화된 군집화 수행을 실행하지 못하는 경우가 발생하게 된다. 따라서 본 논문에서는 원래의 데이터의 구조에 좀더 근접한 퍼지 군집화를 수행하기 위하여 격자를 바탕으로 한 데이터의 밀도를 이용한 FCM을 제안하고, 이러한 밀도 기반 FCM에 의해 결정된 군집의 합병 기법을 제안하였다. N-차원의 데이터 공간을 N-차원의 격자로 나누고, 초기 군집 중심의 개수와 위치는 각 격자의 밀도를 바탕으로 결정된다. 초기화 이후에 각 격자 내부에서 FCM을 이용하여 군집화를 수행하고, 계속해서 이웃 격자의 군집결과에 대하여 군집간의 유사도 측도를 이용하여 군집 합병을 수행함으로써 데이터의 자연적인 구조에 근접한 군집화를 수행하였다. 제안된 군집화 합병 기법의 향상된 성능은 UCI Machine Learning Repository 데이터를 이용하여 확인하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.381-384
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• 2011

최근 무선 센서 네트워크의 수집 데이터에 대해 에너지 효율적인 저장 및 질의 처리를 위한 다양한 연구가 이루어지고 있다. 데이터 중심 저장 (DCS: Data-Centric Storage) 기법은 인-네트워크 방식 기반의 효율적인 데이터 저장과 질의 처리를 위해 제안된 기법이다. DCS 기법은 수집 데이터의 값에 따라 저장 될 위치를 미리 결정하여 각 데이터가 발생시 해당 위치에 인-네트워크 방식으로 저장한다. 이를 통해 질의 처리시 불필요한 질의 배포를 최소화 시킨다. 하지만 기존에 제안된 DCS 기법들은 수집되는 데이터의 발생 범위를 고정적으로 설정한다. 따라서 시기별로 상이한 범위의 데이터가 발생되는 실제 응용에 서는 저장 공간 활용의 불균등을 초래하여 네트워크 수명을 단축시킨다. 본 논문은 시간이 지남에 따라 변화 하는 데이터 발생 패턴에 상황 적응적인 범위 설정 기법을 적용하여 네트워크 전반에 걸쳐 노드들의 저장 공간을 균등하게 사용하는 상황 인지 데이터 중심 저장 방식을 제안한다. 또한 제안하는 기법의 우수성을 보이기 위해 기존 DCS 기법과 성능을 비교평가 한다.