• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.135-139
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• 2007

Recently Data Centric Storage (DCS) schemes are variously studied for several applications (e.g. natural environment investigation, military application systems and environmental changes monitoring). In DCS scheme, data is stored at nodes within the network by name. There are several drawbacks in the existing schemes. The first is the inefficiency of the range query processing on not considered the locality of store point. the second is the non-homogeneity of store load of each sensors in case of the sensor distribution density is non-uniformed. In this paper, we propose a novel data centric storage scheme with the sensor distribution density which satisfied with the locality of data store location. This scheme divides whole sensor network area using grid and distributes the density bit map witch consist of the sensor density information of each cell. sensors use the density bit map for storing and searching the data. We evaluate our scheme with existing schemes. As a result, we show improved load balancing and more efficient range query processing than existing schemes in environment which sensors are distributed non-uniform.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06a
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• pp.234-235
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• 2010

• The KIPS Transactions:PartB
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• v.16B no.3
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• pp.247-256
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• 2009

Typical pseudo-relevance feedback methods assume the top-retrieved documents are relevant and use these pseudo-relevant documents to expand terms. The initial retrieval set can, however, contain a great deal of noise. In this paper, we present a cluster-based resampling method to select better pseudo-relevant documents based on the relevance model. The main idea is to use document clusters to find dominant documents for the initial retrieval set, and to repeatedly feed the documents to emphasize the core topics of a query. Experimental results on large-scale web TREC collections show significant improvements over the relevance model. For justification of the resampling approach, we examine relevance density of feedback documents. The resampling approach shows higher relevance density than the baseline relevance model on all collections, resulting in better retrieval accuracy in pseudo-relevance feedback. This result indicates that the proposed method is effective for pseudo-relevance feedback.

• Journal of KIISE:Databases
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• v.31 no.1
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• pp.13-22
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• 2004

With the proliferation of multimedia data, there is an increasing need to support the indexing and retrieval of high-dimensional image data. Although there have been many efforts, the performance of existing multidimensional indexing methods is not satisfactory in high dimensions. Thus the dimensionality reduction and the approximate solution methods were tried to deal with the so-called dimensionality curse. But these methods are inevitably accompanied by the loss of precision of query results. Therefore, recently, the vector approximation-based methods such as the VA- file and the LPC-file were developed to preserve the precision of query results. However, the performance of the vector approximation-based methods depend largely on the size of the approximation file and they lose the advantages of the multidimensional indexing methods that prune much search space. In this paper, we propose a new index structure called the GC-tree for efficient similarity search in image databases. The GC-tree is based on a special subspace partitioning strategy which is optimized for clustered high-dimensional images. It adaptively partitions the data space based on a density function and dynamically constructs an index structure. The resultant index structure adapts well to the strongly clustered distribution of high-dimensional images.

• Journal of KIISE:Databases
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• v.32 no.5
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• pp.547-557
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• 2005

The purpose of selectivity estimation is to maintain the summary data in a very small memory space and to minimize the error of estimated value and query result. In case of estimating selectivity for large spatial data, the existing works need summary information which reflect spatial data distribution well to get the exact result for query. In order to get such summary information, they require a much memory space. Therefore In this paper, we propose a new technique cumulative density wavelet Histogram, called CDW Histogram, which gets a high accurate selectivity in small memory space. The proposed method is to utilize the sub-histograms created by CD histogram. The each sub-histograms are used to generate the wavelet summary information by applying the wavelet transform. This fact gives us good selectivity even if the memory sire is very small. The experimental results show that the proposed method simultaneously takes full advantage of their strong points - gets a good selectivity using the previous histogram in ($25\%\~50\%$) memory space and is superior to the existing selectivity estimation techniques. The proposed technique can be used to accurately quantify the selectivity of the spatial range query in databases which have very restrictive memory.

• Journal of Information Processing Systems
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• v.2 no.2
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• pp.67-75
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• 2006

In order to acquire a precise and quick response to an analytical query, proper selection of the views to materialize in the data warehouse is crucial. In traditional view selection algorithms, all relations are considered for selection as materialized views. However, materializing all relations rather than a part results in much worse performance in terms of time and space costs. Therefore, we present an improved algorithm for selection of views to materialize using the clustering method to overcome the problem resulting from conventional view selection algorithms. In the presented algorithm, ASVMRT (Algorithm for Selection of Views to Materialize using Reduced Table), we first generate reduced tables in the data warehouse using clustering based on attribute-values density, and then we consider the combination of reduced tables as materialized views instead of a combination of the original base relations. For the justification of the proposed algorithm, we reveal the experimental results in which both time and space costs are approximately 1.8 times better than conventional algorithms.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.348-351
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• 2009

유비쿼터스 사회를 실현하는 핵심기술인 u-GIS 공간정보 기술은 데이터 스트림 처리 시스템(Data Stream Management System)과 지리정보 시스템(Geography Information System)이 결합된 플랫폼인 u-GIS DSMS를 요구한다. u-GIS DSMS는 GeoSeonsor에서 수집되는 센서 테이터와 GIS의 공간정보 데이터를 결합하여 처리하는 공간영역질의가 다수 요구된다. 이런 공간영역질의들은 특정 지역에 밀집하게 등록되는 경향이 있으며, 유사한 프리디킷을 가질 가능성이 높다. 이러한 특징은 공간영역질의가 특정 지역에 밀집되면 다수의 비슷한 연산들이 반복적으로 처리하기 때문에 시스템 성능이 저하 될 것이다. 이를 해결하기 위해 영역질의 색인기법 연구가 활발히 진행되고 있다. 그러나 기존의 VCR-Index와 CQI-Index 기법은 질의영역을 셀 구조나 가상구조로 분할하여 처리하기 때문에 자원 및 연산을 공유 할 수 없어 질의 처리 속도가 현저히 저하되기 때문에 대량의 공간영역질의 처리에는 부적합하다. 그래서 본 논문에서는 공간영역질의의 효율적인 연산 공유를 위한 질의영역 밀집도 기반의 그룹화 기법을 제안한다. 이 기법은 질의영역의 밀집도를 이용하여 공간영역질의들을 그룹화 후 색인을 구성한다. 색인된 영역들의 데이터는 단일 큐로 구성 후 질의들의 프리디킷을 분석하여 자원 및 연산 공유기법을 통해 기존의 기법보다 처리 속도 향상 및 메모리 사용을 감소시켰다.

• Journal of Korea Multimedia Society
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• v.8 no.3
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• pp.297-312
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• 2005

Aggregation is an operation that plays a key role in multidimensional OLAP (MOLAP) systems of data warehouse. Existing aggregation operations in MOLAP have been proposed for file structures such as multidimensional arrays. These tile structures do not work well with skewed distributions. This paper presents a physical design methodology for storage structures ni MOLAP that use the multidimensional tile organizations adapting to a skewed distribution. In uniform data distribution, we first show that the performance of multidimensional analytical processing is highly affected by the similarity of the shapes between query regions and page regions in the domain space of the multidimensional file organizations. And than, in skewed distributions, we reflect the effect of data distributions on the design by using the shapes of the normalized query regions that are weighted with data density of those query regions. Finally, we demonstrate that the physical design methodology theoretically derived is indeed correct in real environments. In the two-dimensional file organizations, the results of experiments indicate that the performance of the proposed method is enhanced by more than seven times over the conventional method. We expect that the performance will be more enhanced when the dimensionality is more than two. The result confirms that the proposed physical design methodology is useful in a practical way.

• Journal of the Korea Convergence Society
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• v.7 no.6
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• pp.267-274
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• 2016

This study is planned to investigate the change in blood limpid and oxygen-carrying factors of middle-aged women through 12 weeks of circuit weight training. The participants worked out three times a week for 50 minutes each, with 50-80% of 1RM intensity. As such, the purpose of the study and the procedure brought about the following conclusions. First, the query results of blood limpid showed that the total amount of cholesterol, triglycerides in the blood, and low-density lipoprotein cholesterol decreased significantly, and the amount of high density lipoprotein cholesterol did not show an increase. Second, the results of oxygen-carrying factors showed that the number of oxygen-carrying red blood cells, hemoglobin, and hematocrit showed an increase. Therefore, this circuit weight training program which used weights of the geological landscape is considered as an effective way to exercise, since it had a positive impact on the oxygen-carrying capacity and cardiovascular disease prevention

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.2
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• pp.235-242
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• 2012

In RFID System, one of the problem that we must slove is to devise a good anti-collision algorithms to improve the efficiency of tag identification which is usually low because of tag collision. Among of the existing RFID anti-collision algorithm, BS (Binary Search) algorithm, though simple, has a disadvantage that the stage of times used to identify the tags increase exponentially as the number of tags does. In this paper, I propose a new anti-collision algorithm called Multi-collision reflected frame which restricts the number of stages and decided bit. Since the proposal algorithm keep the length size of UID and density of total tag when have 100%. The simulation results showed that the proposed algorithm improves the efficiency by 30~50% compared to the existing algorithm.