• Journal of KIISE:Computing Practices and Letters
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• v.16 no.12
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• pp.1234-1238
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• 2010

In recent, there have been done many studies on applications that monitor the information of mobile objects using Wireless Sensor Networks (WSN). A density query that finds out an area spread by density that a target object requires in the whole sensing field is a field of object monitoring applications. In this paper, we propose a novel homogeneous network-based in-network density query processing scheme that significantly reduces query processing costs and assures high accuracy. This scheme is based on the possibility-based expected region selection technique and the result compensation technique in order to enhance the accuracy of the density query and to minimize its energy consumption. To show the superiority of our proposed scheme, we compare it with the existing density query processing scheme. As a result, our proposed scheme reduces about 92% energy consumption for query processing, while its network lifetime increases compared to the existing scheme. In addition, the proposed scheme guarantees higher accuracy than the existing scheme in terms of the query result.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.721-725
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• 2004

Multiple-count problem is occurred when rectangle objects span across several buckets. The Cumulative Density (CD) histogram is a technique which solves multiple-count problem by keeping four sub-histograms corresponding to the four points of rectangle. Although it provides exact results with constant response time, there is still a considerable issue. Since it is based on a query window which aligns with a given grid, a number of errors may be occurred when it is applied to real applications. In this paper, we proposed selectivity estimation techniques using the generalized cumulative density histogram based on two probabilistic models: (1) probabilistic model which considers the query window area ratio, (2) probabilistic model which considers intersection area between a given grid and objects. In order to evaluate the proposed methods, we experimented with real dataset and experimental results showed that the proposed technique was superior to the existing selectivity estimation techniques. The proposed techniques can be used to accurately quantify the selectivity of the spatial range query on rectangle objects.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.260-262
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• 2004

Spatial database system provides many query types and most of them are required frequent disk I/O and much CPU time. k-NN search is to find k-th closest object from the query point and up to now, several k-NN search methods have been proposed. Among these, MINMAX distance method has an aim not to visit unnecessary node by applying pruning technique. But this method access more disk than necessary while pruning unnecessary node. In this paper, we propose new k-NN search algorithm based on density of object. With this method, we predict the radius to be expected to contain k-NN object using density of data set and search those objects within this radius and then adjust radius if failed. Experimental results show that this method outperforms the previous MINMAX distance method. This algorithm visit fewer disks than MINMAX method by the factor of maximum $22\%\;and\;average\;6\%.$

• The KIPS Transactions:PartD
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• v.11D no.4
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• pp.983-990
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• 2004

Multiple-count problem is occurred when rectangle objects span across several buckets. The CD histogram is a technique which selves this problem by keeping four sub-histograms corresponding to the four points of rectangle. Although It provides exact results with constant response time, there is still a considerable issue. Since it is based on a query window which aligns with a given grid, a number of errors nay be occurred when it is applied to real applications. In this paper, we propose selectivity estimation techniques using the generalized cumulative density histogram based on two probabilistic models : \circled1 probabilistic model which considers the query window area ratio, \circled2 probabilistic model which considers intersection area between a given grid and objects. Our method has the capability of eliminating an impact of the restriction on query window which the existing cumulative density histogram has. We experimented with real datasets to evaluate the proposed methods. Experimental results show that the proposed technique is superior to the existing selectivity estimation techniques. Furthermore, selectivity estimation technique based on probabilistic model considering the intersection area is very accurate(less than 5% errors) at 20% query window. The proposed techniques can be used to accurately quantify the selectivity of the spatial range query on rectangle objects.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.361-364
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• 2023

볼륨 렌더링은 3D 밀도 데이터를 가시화 할 때 활용되는 기술로써 이 알고리즘에서 중요한 것은 렌더링 시간 단축이며, 본 논문에서는 이 계산시간을 효율적으로 개선시킬 수 있는 방법을 제시한다. 렌더링의 처리시간은 탐색하는 횟수에 따라 결과 차이가 발생하지만, 탐색 횟수가 적을 경우 렌더링의 품질이 저하되고 반대인 경우에는 화질의 표현력은 높으나 많은 처리시간이 소요된다. 따라서 화질이 떨어지지 않는 최소의 탐색 방법이 요구되므로 본 논문에서는 밀도의 탐색 최적화와 시간별 밀도가 존재하는 위치를 예측하여 계산을 효율적으로 처리 할 수 있는 PyCUDA 프레임워크에 대해서 소개한다.

• Journal of applied mathematics & informatics
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• v.41 no.6
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• pp.1327-1339
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• 2023

World Wide Web (WWW) use has been increasing recently due to users needing more information. Lately, there has been a growing trend in the document information available to end users through the internet. The web's document search process is essential to find relevant documents for user queries.As the number of general web pages increases, it becomes increasingly challenging for users to find records that are appropriate to their interests. However, using existing Document Information Retrieval (DIR) approaches is time-consuming for large document collections. To alleviate the problem, this novel presents Spatial Clustering Ranking Pattern (SCRP) based Density Ant Colony Information Retrieval (DACIR) for user queries based DIR. The proposed first stage is the Term Frequency Weight (TFW) technique to identify the query weightage-based frequency. Based on the weight score, they are grouped and ranked using the proposed Spatial Clustering Ranking Pattern (SCRP) technique. Finally, based on ranking, select the most relevant information retrieves the document using DACIR algorithm.The proposed method outperforms traditional information retrieval methods regarding the quality of returned objects while performing significantly better in run time.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.3
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• pp.2158-2164
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• 2015

In u-GIS environments, various load shedding techniques have been researched in order to balance loads caused by input spatial data streams. However, typical load shedding methods on aspatial data lack regard for characteristics of spatial data, also previous load shedding approaches on spatial, which still lack regard for spatial data density or dynamic input data stream, give rise to troubles on spatial query processing performance and accuracy. Therefore, dynamic load shedding scheme over spatial data stream is proposed through stored spatial data deviation and load ratio of input data stream in order to improve spatial continuous query accuracy and performance in u-GIS environment. In proposed scheme, input data which are a big probability related to spatial continuous query may be a strong chance to be dropped relatively.

• Journal of KIISE:Information Networking
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• v.32 no.4
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• pp.525-534
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• 2005

This paper is concerned with the query slipping and its prevention for trajectory-based matchmaking service in wireless sensor networks. The problem happens when a query propagating along a subscribe trajectory moves through a publish trajectory without obtaining desired information, even though two trajectories intersect geometrically. There follows resubmission of the query or initiation of another subscribe trajectory Thus, query slipping results in considerable time delay and in the worst, looping in the trajectory or query flooding the network. We address the problem formally and suggest a solution. First, the area where nodes are distributed is logically partitioned into smaller grids, and a grid-based multicast next-hop selection algorithm is proposed. Our algorithm not only attempts to make the trajectory straight but also considers the nodal density of recipient nodes and the seamless grid-by-grid multicast. We prove that the publishing and subscribing using the algorithm eventually eliminate the possibility of the slipping. It toms out that our algorithm dissipates significantly less power of neighbor nodes, compared to the non grid-based method, as greedy forwarding, and the fixed- sized grid approach, as GAF (Geographical Adaptive Fidelity)

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.4
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• pp.59-70
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• 2003

Spatial data base system provides many query types and most of them are required frequent disk I/O and much CPU time. k-NN search is to find k-th closest object from the query point and up to now, several k-NN search methods have been proposed. Among these, MINMAX distance method has an aim not to access unnecessary node by adapting pruning technique. But this method accesses more disks than necessary while pruning unnecessary nodes. In this paper, we propose new k-NN search algorithm based on density of object. With this method, we predict the radius to be expected to contain k-NN objects using density of data set and search those objects within this radius and then adjust radius if failed. Experimental results show that this method outperforms the previous MINMAX distance method. This algorithm visit less disks than MINMAX method by the factor of maximum 22% and average 7%.

• Journal of Multimedia Information System
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• v.6 no.1
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• pp.37-42
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• 2019

Skyline queries are used in a variety of fields to make optimal decisions. However, as the volume of data and the dimension of the data increase, the number of skyline points increases with the amount of time it takes to discover them. Mainly, because the number of skylines is essential in many real-life applications, various studies have been proposed. However, previous researches have used the k-parameter methods such as top-k and k-means to discover representative skyline points (RSPs) from entire skyline point set, resulting in high query response time and reduced representativeness due to k dependency. To solve this problem, we propose a new Connected Component Clustering based Representative Skyline Query (3CRS) that can discover RSP quickly even in high-dimensional data through connected component clustering. 3CRS performs fast discovery and clustering of skylines through hash indexes and connected components and selects RSPs from each cluster. This paper proves the superiority of the proposed method by comparing it with representative skyline queries using k-means and DBSCAN with the real-world dataset.