• Spatial Information Research
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• v.18 no.1
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• pp.77-87
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• 2010

Spatio-temporal join operators are essential to the management of spatio-temporal data such as moving objects. For example, the join operators are parts of processing to analyze movement of objects and search similar patterns of moving objects. Various studies on spatio-temporal join queries in outdoor space have been done. Recently with advance of indoor positioning techniques, location based services are required in indoor space as well as outdoor space. Nevertheless there is no one about processing of spatio-temporal join query in indoor space. In this paper, we introduce continuous spatio-temporal self-join queries in indoor space and propose a method of processing of the join queries over stream data of moving objects. The continuous spatio-temporal self-join query is to update the joined result set satisfying spatio-temporal predicates continuously. We assume that positions of moving objects are represented by symbols such as a room or corridor. This paper proposes a data structure, called Candidate Pairs Buffer, to filter and maintain massive stream data efficiently and we also investigate performance of proposed method in experimental study.

• 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.

• Journal of KIISE:Databases
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• v.32 no.4
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• pp.345-361
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• 2005

A transform-space index indexes objects represented as points in the transform space An advantage of a transform-space index is that optimization of join algorithms using these indexes becomes relatively simple. However, the disadvantage is that these algorithms cannot be applied to original-space indexes such as the R-tree. As a way of overcoming this disadvantages, the authors earlier proposed the transform-space view join algorithm that joins two original- space indexes in the transform space through the notion of the transform-space view. A transform-space view is a virtual transform-space index that allows us to perform join in the transform space using original-space indexes. In a transform-space view join algorithm, the order of accessing disk pages -for which various space filling curves could be used -makes a significant impact on the performance of joins. In this paper, we Propose a new space filling curve called the adaptive row major order (ARM order). The ARM order adaptively controls the order of accessing pages and significantly reduces the one-pass buffer size (the minimum buffer size required for guaranteeing one disk access per page) and the number of disk accesses for a given buffer size. Through analysis and experiments, we verify the excellence of the ARM order when used with the transform-space view join. The transform-space view join with the ARM order always outperforms existing ones in terms of both measures used: the one-pass buffer size and the number of disk accesses for a given buffer size. Compared to other conventional space filling curves used with the transform-space view join, it reduces the one-pass buffer size by up to 21.3 times and the number of disk accesses by up to $74.6\%$. In addition, compared to existing spatial join algorithms that use R-trees in the original space, it reduces the one-pass buffer size by up to 15.7 times and the number of disk accesses by up to $65.3\%$.

• Journal of KIISE:Databases
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• v.31 no.4
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• pp.352-361
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• 2004

Spatial joins find all pairs of spatial objects that satisfy a given spatial relationship. In this paper, we propose the transformation-based spatial partition join algorithm (TSPJ), a new spatial join algorithm that performs join in the transform space without using indexes. Since the existing algorithms deal with extents of spatial objects in the original space, they either need to replicate the spatial objects or have a relatively complex partition structure-resulting in degrading performance. In contrast, TSPJ transforms objects in the original space into points in the transform space and deals only with points having no extents. The transformation does not incur any additional overhead. Thus, our algorithm has advantages over existing ones in that it obviates the need for replicating spatial objects, and its partition structure is simple. As a result, it always has better performance compared with existing algorithms. Extensive experiments show that TSPJ improves performance by 20.5∼38.0% over the existing algorithms compared.

• Journal of Computing Science and Engineering
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• v.1 no.2
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• pp.177-194
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• 2007

Index selection is one of the most important decisions to take in the physical design of relational data warehouses. Indices reduce significantly the cost of processing complex OLAP queries, but require storage cost and induce maintenance overhead. Two main types of indices are available: mono-attribute indices (e.g., B-tree, bitmap, hash, etc.) and multi-attribute indices (join indices, bitmap join indices). To optimize star join queries characterized by joins between a large fact table and multiple dimension tables and selections on dimension tables, bitmap join indices are well adapted. They require less storage cost due to their binary representation. However, selecting these indices is a difficult task due to the exponential number of candidate attributes to be indexed. Most of approaches for index selection follow two main steps: (1) pruning the search space (i.e., reducing the number of candidate attributes) and (2) selecting indices using the pruned search space. In this paper, we first propose a data mining driven approach to prune the search space of bitmap join index selection problem. As opposed to an existing our technique that only uses frequency of attributes in queries as a pruning metric, our technique uses not only frequencies, but also other parameters such as the size of dimension tables involved in the indexing process, size of each dimension tuple, and page size on disk. We then define a greedy algorithm to select bitmap join indices that minimize processing cost and verify storage constraint. Finally, in order to evaluate the efficiency of our approach, we compare it with some existing techniques.

• Journal of KIISE:Databases
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• v.34 no.5
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• pp.420-436
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• 2007

The choice of an effective indexing method is crucial to guarantee the performance of the spatial join operator which is heavily used in geographical information systems. The $R^*$-tree based method is renowned as one of the most representative indexing methods. In this paper, we propose an efficient spatial join technique based on the DOT(Double Transformation) index, and compare it with the spatial Join technique based on the $R^*$-tree index. The DOT index transforms the MBR of an spatial object into a single numeric value using a space filling curve, and builds the $B^+$-tree from a set of numeric values transformed as such. The DOT index is possible to be employed as a primary index for spatial objects. The proposed spatial join technique exploits the regularities in the moving patterns of space filling curves to divide a query region into a set of maximal sub-regions within which space filling curves traverse without interruption. Such division reduces the number of spatial transformations required to perform the spatial join and thus improves the performance of join processing. The experiments with the data sets of various distributions and sizes revealed that the proposed join technique is up to three times faster than the spatial join method based on the $R^*$-tree index.

• Journal of applied mathematics & informatics
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• v.10 no.1_2
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• pp.209-216
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• 2002

We study correspondences between interval structures and hyperstructures.

• Communications of the Korean Mathematical Society
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• v.11 no.1
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• pp.225-233
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• 1996

It is shown that for any space A, the cofibration X \to X \Join \sumA \to \sumA \wedge X$ decomposable when X is a co-T-space. It is also obtain necessary and sufficient conditions for the cofibration $X \to X \Join A \to A \wedge X$ is trivial, in the sense of cofibre homotopy type.

• Journal of KIISE:Databases
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• v.29 no.1
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• pp.72-78
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• 2002

The distance join is a spatial join which finds data pairs in the order of distance between two spatial data sets using R-trees. The distance join stores node pairs in a priority queue, which are retrieved while traversing R-trees in a top-town manner, in the order of distance. This paper first shows that a priority strategy for the tied pairs in the priority queue during distance join processing has much effect on its performance, and then proposes an optimized secondary priority method. The experiments show that the proposed method is always better than the other methods in the performance perspectives.

• The Journal of Korean Association of Computer Education
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• v.6 no.4
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• pp.11-21
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• 2003

In rapidly changing an information=oriented society, it is essential to control massive document information by electronic file. In relation to these electronic document, it is also important to keep and maintain all kinds of information without any losses. It should be allowed to trace previous contents as well as recently updated contents by controlling updated contents with version. For these, XML is recommendable. In this thesis, we intend to save the document storing space by saving only updated contents with version without saving whole documentation, when document is updated. In case of controlling the history of document update by version, we designed system so as to omit "JOIN operation" if document size is under a certainspecific size. Therefore, we implemented a new XML document repository system which is possible for quick search and efficient XML document saving by reducing perfomance deterioration caused by JOIN operation.