• 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.

• Spatial Information Research
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• v.16 no.2
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• pp.219-236
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• 2008

Data stream collected from sensors contain a large amount of useful information including causality relationships. The causality join query for data stream is to retrieve a set of pairs (cause, effect) from streams of data. A part of causality pairs may however be lost from the query result, due to the delay from sensors to a data stream management system, and the limited size of sliding windows. In this paper, we first investigate spatial, temporal, and spatio-temporal aspects of the causality join query for data stream. Second, we propose several strategies for sliding window management based on these observations. The accuracy of the proposed strategies is studied by intensive experiments, and the result shows that we improve the accuracy of causality join query in data stream from simple FIFO strategy.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.223-224
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• 2021

Recently, k-farthest neighbor (kFN) queries have not as much attention as k-nearest neighbor (kNN) queries. Therefore, this study considers moving k-farthest neighbor (MkFN) queries for spatial network databases. Given a positive integer k, a moving query point q, and a set of data points P, MkFN queries can constantly retrieve k data points that are farthest from the query point q. The challenge with processing MkFN queries in spatial networks is to avoid unnecessary or superfluous distance calculations between the query and associated data points. This study proposes a batch processing algorithm, called MOFA, to enable efficient processing of MkFN queries in spatial networks. MOFA aims to avoid dispensable distance computations based on the clustering of both query and data points. Moreover, a time complexity analysis is presented to clarify the effect of the clustering method on the query processing time. Extensive experiments using real-world roadmaps demonstrated the efficiency and scalability of the MOFA when compared with a conventional solution.

• Journal of Korea Spatial Information System Society
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• v.10 no.1
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• pp.15-28
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• 2008

In ubiquitous sensor networks(USN) environment, it is essential to process region query for user-demand services. Using R-tree is a preferred technique to process region query for in-network query environment. In USN environment, USN middleware must select sensors that transfers region query with accuracy because the lifetime of sensors is that of whole sensor networks. When R-tree is used, however, it blindly passes the region query including non-existent sensors where MBR(Minimum Boundary Rectangle) of R-tree is Intersected by region of query. To solve in this problem, we propose a reconstruction of region query method which is a trie-structured Quad tree in the base station that includes sensors in region of query select with accuracy. We observed that the proposed method delays response time than R-tree, but is useful for reducing communication cost and energy consumption.

• 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.

• Journal of Communications and Networks
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• v.13 no.4
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• pp.400-407
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• 2011

Various air indexing and data scheduling schemes for wireless broadcast of spatial data have been developed for energy efficient query processing. The existing schemes are not effective when the clients' data access patterns are skewed to some items. It is because the schemes are based on flat broadcast that does not take the popularity of the data items into consideration. In this paper, thus, we propose a data scheduling scheme letting the popular items appear more frequently on the channel, and grid-based distributed index for non-flat broadcast (GDIN) for window query processing. The proposed GDIN allows quick and energy efficient processing of window query, matching the clients' linear channel access pattern and letting the clients access only the queried data items. The simulation results show that the proposed GDIN significantly outperforms the existing schemes in terms of access time, tuning time, and energy efficiency.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.564-566
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• 2003

There are no immutable phenomena in reality. A lot of applications are dealing with data characterized by spatial and temporal and/or uncertain features. Currently, there has no any data model accommodating enough those three elements of spatial objects to directly use in application systems. For such reasons, we introduce a fuzzy spatio -temporal data model (FSTDM) and a method of integrating temporal and fuzzy spatial operators in a unified manner to create fuzzy spatio -temporal (FST) operators. With these operators, complex query expression will become concise. Our research is feasible to apply to the management systems and query processor of natural resource data, weather information, graphic information, and so on.

• Journal of Korea Multimedia Society
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• v.6 no.3
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• pp.384-396
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• 2003

Hybrid query processing method is used for preventing server overload that is created by heavy user connection in Web GIS. In Hybrid query processing method, both server and client participate in spatial query processing. But, Hybrid query processing method is restricted in scalability of server and it can't be fundamentally solution for server overload. So, it is necessary for Web GIS to be brought in web clustering technique. In this thesis, we propose load-balancing method that uses proximity of query region. In this paper, we create tile groups that have relation each tile in same group is very close, and forward client request to the server that can have maximum rate of buffer reuse with considering characteristic of spatial query. With out load balancing method, buffet in server is optimized for exploring spatial index tree and increase rate of buffer reuse, so it can be reduced amount of disk access and increase system performance.

• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.203-209
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• 2012

Recently, k-nearest neighbors query methods based on wireless broadcasting environment are actively studied. The advantage of wireless broadcasting environment is the scalability that enables collective query processing for unspecified users connected to the server. However, in case existing k-NN query is applied in wireless broadcasting environment, there can be a disadvantage that backtracking may occur and consequently the query processing time is increasing. In this paper proposes a hierarchical bitmap-based spatial index in order to efficiently process the k-NN queries in wireless broadcasting environment. HBI reduces the bitmap size using such bitmap information and tree structure. As a result, reducing the broadcast cycle can reduce the client's tuning time and query processing time. In addition, since the locations of all the objects can be detected using bitmap information, it is possible to tune to necessary data selectively. For this paper, a test was conducted implementing HBI to k-NN query and the proposed technique was proved to be excellent by a performance evaluation.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.2
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• pp.23-32
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• 2004

The query optimizer's important task while a query is invoked is to estimate the fraction of records in the databases that satisfy the given query condition. The query result size estimation in spatial databases, like relational databases, proceeds to partition the whole input into a small number of subsets called “buckets” and then estimate the fraction of the input in the buckets. The accuracy of estimation is determined by the difference between the real data counts and approximations in the buckets, and is dependent on how to partition the buckets. Existing techniques for spatial databases are equi-area and equi-count techniques, which are respectively analogous in relation databases to equi-height histogram that divides the input value range into buckets of equal size and equi-depth histogram that is equal to the number of records within each bucket. In this paper we propose a new partitioning technique that determines buckets according to the maximal difference of area which is defined as the product of data ranges End frequencies of input. In this new technique we consider both data values and frequencies of input data simultaneously, and thus achieve substantial improvements in accuracy over existing approaches. We present a detailed experimental study of the accuracy of query result size estimation comparing the proposed technique and the existing techniques using synthetic as well as real-life datasets. Experiments confirm that our proposed techniques offer better accuracy in query result size estimation than the existing techniques for space query size, bucket number, data number and data size.