• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.34-36
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• 2005

선택도 추정 기법은 상용 데이터베이스에서 질의 최적화를 위해 많이 사용하고 있다. 그 중 선택도 추정 기법에 가장 많이 사용되고 있는 기법은 히스토그램이다. 최근 시공간 데이터베이스 관련 연구에서 시간$\cdot$공간 데이터베이스의 선택도 추정 기법이 활발하게 이루어지고 있다. 이 히스토그램 추정 기법이 과거에서 현재시점까지 범위 질의 수행을 성공적으로 이루어지고 있지만 대량의 데이터들을 효율적으로 관리하기에는 저장오버헤드가 너무 크다. 본 논문에서는 시공간데이터베이스에서 성공적으로 선택도 추정을 다룬 히스토그램 추정 기법을 보완하여 과거 이력데이터들의 저장을 효율적으로 할 수 있는 압축기법을 제안한다. 현재 객체에 대해서는 기존 연구에서 성공적으로 이루어진 히스토그램 기반 추정 기법을 응용하고 과거 이력데이터에 대해서는 압축기법인 웨이블렛을 응용하여 선택도추정의 오류율과 저장오버헤드의 향상이 기대된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.11a
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• pp.63-66
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• 2004

최근 시공간 데이터에 대한 관심이 늘어나면서 시간에 따라 이동하는 객체에 대한 처리와 이력 공간 정보를 처리하는 영역에 대한 연구가 활발하게 일어나고 있다. 이러한 시공간 데이터베이스에서의 효과적인 질의 처리를 위해 선택도 추정 기술의 요구가 생겨났다. 본 논문에서는 이력 공간 정보에 대한 선택도 추정을 위하여 T-Minskew 히스토그램을 구축하고 이를 이용하여 선택도 추정 방법을 제시한다. 또한 임계치 기법을 이용한 효과적인 히스토그램 유지 기법을 제시한다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.82-84
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• 2003

위치 기반 서비스 시스템은 이동객체에 대한 효율적인 검색을 가능하게 해주는 시스템으로 이동객체의 현재와 과거 위치에 대한 정보를 유지한다. 이러한 시스템을 평가하기 위해서는 이동객체들의 사실적인 움직임을 기술한 데이타 집합이 필요하다. 이러한 데이타 집합을 생성하는 대표적인 생성기로는 GSTD가 있다. 하지만 GSTD도 몇 가지 문제점을 지니고 있다. GSTD에 의해서 생성되는 작업공간과 데이타 집합은 실생활 객체의 움직임을 묘사하는데 부적절한 면이 있다. 또한 GSTD가 제공하는 매개변수 안에 사용자가 객체의 이동성과 시간성을 명확히 기술 해줄 수 있는 부분이 제시 되어 있지 않다. 본 논문에서는 GSTD의 여러 매개변수들과 함수들을 변환하거나 추가하여 좀 더 현실적인 객체 움직임을 나타내는 데이타 집합을 만들고, 사용자가 객체의 특성을 세밀히 지정해 줄 수 있는 확장된 GSTD를 구현한다. 또한 확장된 GSTD 기반 LBS 시뮬레이터의 설계와 구현에 관한 원리를 포함한 주요 특징을 기술한다. 확장된 GSTD는 다양한 LBS 응용 시스템의 성능 측정에 활용될 수 있을 것이다.

• Journal of KIISE:Databases
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• v.31 no.2
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• pp.150-162
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• 2004

Moving objects have characteristics that they change continuously their positions over time. The movement of moving objects should be stored on trajectories for processing past queries. Moving objects databases need to provide spatio-temporal index for handling moving objects queries like combined queries. Combined queries consist of a range query selecting trajectories within a specific range and a trajectory query extracting to parts of the whole trajectory. Access methods showing good performance in range queries have a shortcoming that the cost of processing trajectory Queries is high. On the other hand, trajectory-based index schemes like the TB-tree are not suitable for range queries because of high overlaps between index nodes. This paper proposes new TR(Trajectory Riving)-tree which is revised for efficiently processing the combined queries. This index scheme has several features like the trajectory preservation, the increase of the capacity of leaf nodes, and the logical trajectory riving in order to reduce dead space and high overlap between bounding boxes of nodes. In our Performance study, the number of node access for combined queries in TR-tree is about 25% less than the STR-tree and the TB-tree.

• The KIPS Transactions:PartD
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• v.15D no.4
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• pp.451-462
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• 2008

Recently, with the advent of applications using locations of moving objects, it becomes crucial to develop efficient index schemes for spatio-temporal databases. The $TPR^*$-tree is most popularly accepted as an index structure for processing future-time queries. In the $TPR^*$-tree, the future locations of moving objects are predicted based on the CBR(Conservative Bounding Rectangle). Since the areas predicted from CBRs tend to grow rapidly over time, CBRs thus enlarged lead to serious performance degradation in query processing. Against the problem, we propose a new method to adjust CBRs to be tight, thereby improving the performance of query processing. Our method examines whether the adjustment of a CBR is necessary when accessing a leaf node for processing a user query. Thus, it does not incur extra disk I/Os in this examination. Also, in order to make a correct decision, we devise a cost model that considers both the I/O overhead for the CBR adjustment and the performance gain in the future-time owing to the CBR adjustment. With the cost model, we can prevent unusual expansions of BRs even when updates on nodes are infrequent and also avoid unnecessary execution of the CBR adjustment. For performance evaluation, we conducted a variety of experiments. The results show that our method improves the performance of the original $TPR^*$-tree significantly.

• Journal of the Korea Society of Computer and Information
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• v.11 no.6 s.44
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• pp.9-17
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• 2006

Moving objects database systems manage a set of moving objects which changes its locations and directions continuously. The traditional spatial indexing scheme is not suitable for the moving objects because it aimed to manage static spatial data. Because the location of moving object changes continuously, there is problem that expense that the existent spatial index structure reconstructs index dynamically is overladen. In this paper, we analyzed the insertion/deletion costs for processing the movement of objects. The results of our extensive experiments show that the Dynamic Hashing Index outperforms the original R-tree and the fixed grid typically by a big margin.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.6
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• pp.1266-1272
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• 2004

Location-Based Services(LBS) give rise to location-dependent queries of which results depend on the positions of moving objects. Because positions of moving objects change continuously, indexes of moving object must perform update operations frequently for keeping the changed position information. Existing spatial index (Grid File, R-Tree, KDB-tree etc.) proposed as index structure to search static data effectively. There are not suitable for index technique of moving object database that position data is changed continuously. In this paper, I propose a dynamic hashing index that insertion/delete costs are low. The dynamic hashing structure is that apply dynamic hashing techniques to combine a hash and a tree to a spatial index. The results of my extensive experiments show the dynamic hashing index outperforms the $R^$ $R^*$-tree and the fixed grid.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.2
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• pp.364-370
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• 2014

The management and analysis of spatio-temporal and multimedia data is a hot issue in database research because such data types are handled in manny applications. Querying databases of such a content is very important for these applications. This paper addresses algorithms that make index structure by using Douglas-Peucker Algorithm and process range query efficiently on moving objects trajectories. We compare and analyze our algorithms and MBR by experiments. Our algorithms make smaller size of index structure and process more efficiently.

• Journal of Korea Spatial Information System Society
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• v.7 no.1 s.13
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• pp.13-24
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• 2005

Objects, which change their positions over time such as cars, are called moving objects. Trajectories of a moving object have large volumes because trajectories are accumulated. Efficient indexing techniques for searching these large volumes of trajectories are needed in the moving object databases. Especially the TB-tree which supports bundling trajectories is suitable for processing combined queries which have 2 steps: first step is selecting trajectories (range search), next is selecting the parts of each trajectory (trajectory search). But the TB-tree has unnecessary disk accesses cause of lack of spatial discrimination in range queries. In this paper, we propose and implement the splitting polity which can reduce dead spaces of non-leaf node in order to process range queries efficiently. The policy has better performance about range queries than the TB-tree as well as the advantages of the TB-tree, such as highly space utilization and efficient trajectory extraction. This paper shows that the newly proposed split policy has better performance in processing the range queries than that of the TB-tree by experimental evaluation.

• Journal of KIISE:Databases
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• v.35 no.2
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• pp.169-181
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• 2008

Because moving objects usually move on spatial networks, efficient trajectory index structures are required to achieve good retrieval performance on their trajectories. However, there has been little research on trajectory index structures for spatial networks such as FNR-tree and MON-tree. But, because FNR-tree and MON-tree are stored by the unit of the moving object's segment, they can't support the whole moving objects' trajectory. In this paper, we propose an efficient trajectory index structure, named Trajectory of Moving objects on Network Tree(TMN Tree), for moving objects. For this, we divide moving object data into spatial and temporal attribute, and preserve moving objects' trajectory. Then, we design index structure which supports not only range query but trajectory query. In addition, we divide user queries into spatio-temporal area based trajectory query, similar-trajectory query, and k-nearest neighbor query. We propose query processing algorithms to support them. Finally, we show that our trajectory index structure outperforms existing tree structures like FNR-Tree and MON-Tree.