• Journal of KIISE:Databases
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• v.27 no.2
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• pp.247-255
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• 2000

Traditional object decomposition techniques do not decompose spatial objects dynamically during spatial joins, because the object decomposition is very expensive. In this paper, we propose a modified object decomposition technique that can be applied in PBSM(Partition Based Spatial Merge-Join). In real-life data, there are much differences among the sizes of objects. We decompose only large objects with great effects on spatial joins. This technique decreases the decomposition cost of objects during spatial joins and enables efficient filter-refinement steps. Experiments show that the PBSM used with our proposed method performs significantly better than the traditional PBSM.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.665-676
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• 2001

The most expensive spatial operation in patial database in a spatial join which computes a combined table of which tuple consists of two tuples of the two tables satisgying a spatial predicate. Although the execution time of sequential processing of a spatial join has been so far considerably improved the response time is not tolerable because of not meeting the requiremetns of interactive users. It is usually appropriate to use parallel processing to improve the performance of spatial join processing. in spatial database the fixed grids which consist of the regularly partitioned cells can be employed the previous works on the spatial joins have not studied the parallel processing of spatial joins using fixed grids. This paper has presented an analytical cost model that estimates the comparative performance of a parallel spatial join algorithm based on the fixed grids in terms of the number of MBR comparisons. disk accesses, and message passing, Several experiments on the synthetic and real datasets show that the proposed analytical model is very accurate. This most model is also expected to used for implementing a very important DBMS component, Called the query processing optimizer.

• Journal of the Korea Computer Industry Society
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• v.2 no.2
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• pp.157-164
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• 2001

This paper studies on the processing of spatial joins. The spatial join operation is divided into filters and refinement steps in general. The processing of spatial joins can be greatly improved by the use of filters that reduce the polygons in order to find the intersecting ones. As a result, three possible sets of answers are identified: the positive one, the negative one and the inconclusive one. To identify all the interesting pairs of polygons with inconclusive answers, it is necessary to have access to the representation of polygons so that an exact geometry test can take place. We introduce a bit-map approximation technique to drastically reduce the computation required by the refinement step during refinement processing. Bit-map representation are used for the description of the internal, the external and the boundary regions of the polygon objects. The proposed scheme increases the chance of trivial acceptance and rejection of data objects, and reduces unnecessary disk accesses in query processing. It has been shown that the reference to the object data file can be cut down by as much as 60%.

• Journal of Korea Spatial Information System Society
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• v.10 no.4
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• pp.59-66
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• 2008

GeoSensor network means sensor network infra and related software of specific form monitoring a variety of circumstances over geospatial. And these GeoSensor network is implemented by mixing data stream with spatial attribute, spatial relation. But, until a recent date sensor network system has been concentrated on a store and search method of sensor data stream except for a spatial information. In this paper, we propose a definition of spatial data stream and its join strategy model at GeoSensor network, which combine data stream with spatial data. Spatial data stream s defining in this paper are dynamic spatial data stream of a moving object type and static spatial data stream of a fixed type. Dynamic spatial data stream is data stream transmitted by moving sensor as GPS, while static spatial data stream is generated by joining a data stream of general sensor and a relation with location values of these sensors. This paper propose joins of dynamic spatial data stream and static spatial data stream, and cost models estimating join cost. Finally, we show verification of proposed cost models and performance by join strategy.

• Journal of Korea Spatial Information System Society
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• v.4 no.1 s.7
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• pp.39-46
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• 2002

In distributed spatial database systems, users nay issue a query that joins two relations stored at different sites. The sheer volume and complexity of spatial data bring out expensive CPU and I/O costs during the spatial join processing. This paper shows a new spatial join method which joins two spatial relation in a parallel way. Firstly, the initial join operation is divided into two distinct ones by partitioning one of two participating relations based on the region. This two join operations are assigned to each sites and executed simultaneously. Finally, each intermediate result sets from the two join operations are merged to an ultimate result set. This method reduces the number of spatial objects participating in the spatial operations. It also reduces the scope and the number of scanning spatial indices. And it does not materialize the temporary results by implementing the join algebra operators using the iterator. The performance test shows that this join method can lead to efficient use in terms of buffer and disk by narrowing down the joining region and decreasing the number of spatial objects.

• Journal of KIISE:Databases
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• v.27 no.2
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• pp.256-268
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• 2000

Multi-way spatial join is a nested expression of two or more spatial joins. It costs much to process multi-way spatial join, but there have not still reported the scheme of parallel processing of multi-way spatial join. In this paper, parallel processing of multi-way spatial join consists of parallel multi-way spatial filter and parallel spatial refinement. Parallel spatial refinement is executed by the following two steps. The first is the generation of a graph used for reducing duplication of both spatial objects and spatial operations from pairs candidate object table that are the results of multi-way spatial filter. The second is the parallel spatial refinement using that graph. Refinement using the graph is proved to be more efficient than the others. In task creation for parallel refinement, minimum duplication partitioning of the Spatial_Obicct_On_Node graph shows best performance.

• Journal of KIISE:Software and Applications
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• v.26 no.10
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• pp.1178-1178
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• 1999

A spatial join has the property that its execution time exponentially increases in proportion to the number of spatial objects. Recently, there have been many attempts for improving the performance of the spatial join by using parallel processing schemes, In the case of executing parallel spatial join using the parallel machine with shared disk architecture, the disk bottleneck of parallel processing of spatial join worsens in comparison with sequential spatial join. This paper presents the algorithms of task creation and assignment to reduce the disk bottleneck caused by accessing the shared disk at the same time, and to minimize message passing between processors, This paper proposes object caching which is a higher level of abstraction than page caching, and uses it to do creation and assignment of tasks according to temporal and spatial localities for minimizing disk access time. The object caching shows the performance improvement of 50%. The task creation and assignment using localities gives the gain of 30% and 20%. Overall performance evaluation of the proposed algorithms shows 7.2 times speed up than those of sequential execution of spatial joins.

• Journal of KIISE:Databases
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• v.27 no.2
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• pp.238-246
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• 2000

The distance join has been introduced previously, which finds nearest pairs in the order of distance incrementally among two spatial data sets built with multidimensional indexes like R-trees. We propose efficient K-distance joins when the number(K) of pairs to find is preset. Especially, we develop a distance join algorithm with bi-directional expansion and optimized plane sweeping using selection method of sweep axis and direction. The experiments on real spatial data sets show that the proposed algorithm is much better than the former algorithms.

• Journal of KIISE:Databases
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• v.30 no.5
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• pp.438-450
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• 2003

Spatial joins find pairs of objects that overlap with each other. In spatial joins using indexes, original-space indexes such as the R-tree are widely used. An original-space index is the one that indexes objects as represented in the original space. Since original-space indexes deal with sizes of objects, it is difficult to develop a formal algorithm without relying on heuristics. On the other hand, transform-space indexes, which transform objects in the original space into points in the transform space and index them, deal only with points but no sites. Thus, spatial join algorithms using these indexes are relatively simple and can be formally developed. However, the disadvantage of transform-space join algorithms is that they cannot be applied to original-space indexes such as the R-tree containing original-space objects. In this paper, we present a novel mechanism for achieving the best of these two types of algorithms. Specifically, we propose a new notion of the transform-space view and present the transform-space view join algorithm(TSVJ). A transform-space view is a virtual transform-space index based on an original-space index. It allows us to interpret on-the-fly a pre-built original-space index as a transform-space index without incurring any overhead and without actually modifying the structure of the original-space index or changing object representation. The experimental result shows that, compared to existing spatial join algorithms that use R-trees in the original space, the TSVJ improves the number of disk accesses by up to 43.1% The most important contribution of this paper is to show that we can use original-space indexes, such as the R-tree, in the transform space by interpreting them through the notion of the transform-space view. We believe that this new notion provides a framework for developing various new spatial query processing algorithms in the transform space.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.174-176
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• 1999

지리 정보 시스템에서 공간 객체들은 크기가 가변적이고 객체의 형태가 일정하지 않을 뿐만 아니라 공간 객체의 분포 또한 일정하지 않다. 공간 조인은 이러한 공간 객체들의 특성으로 인해 비용이 많이 들고, 공간 객체의 분포에 따라 특정 영역에서의 공간 조인 비용이 많이 들 수 있다. 이 논문에서는 공간 객체들의 분포에 따라 한 번의 Disk 접근으로 공간 객체들을 적재할 수 있는 크기로 셀을 동적 분할하는 알고리즘을 제안한다. 제안된 알고리즘을 수행한 후에 생성된 다양한 크기의 셀을 기반으로 공간 조인을 수행한다. 또한 정제 단계에서 공간 객체를 메모리로 적재하는 Disk I/O를 줄이기 위한 방법도 알아본다.