• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.294-297
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• 2000

Among the multi-dimensional query types, ranking query is needed if we want the object one by one until we satisfy for the result. In multi-dimensional indexing structures like R-tree or its variants, not many methods are introduced in this area. In this paper, we introduce new ranking query processing algorithm which use the filtering mechanism in the R-tree variants.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.6
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• pp.435-445
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• 2013

One of the most challenging issues in radio frequency identification (RFID) and near field communications (NFC) is to correctly and quickly recognize a number of tag IDs in the reader's field. Unlike the probabilistic anti-collision schemes, a query tree based protocol guarantees to identify all the tags, where the distribution of tag IDs is assumed to be uniform. However, in real implements, the prefix of tag ID is uniquely assigned by the EPCglobal and the remaining part is sequentially given by a company or manufacturer. In this paper, we propose an enhanced M-ary query tree protocol (EMQT), which effectively reduces unnecessary query-response cycles between similar tag IDs using m-bit arbitration and tag expectation. The theoretical analysis and simulation results show that the EMQT significantly outperforms other schemes in terms of identification time, identification efficiency and communications overhead.

• Journal of Korea Multimedia Society
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• v.12 no.10
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• pp.1374-1385
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• 2009

Recently, advances in speed of the CPU have for out-paced advances in memory speed. Main-memory access is increasingly a performance bottleneck for main-memory database systems. To reduce memory access speed, cache memory have incorporated in the memory subsystem. However cache memories can reduce the memory speed only when the requested data is found in the cache. We propose a new cache sensitive T-tree index structure called as $CST^*$-tree for range query search. The $CST^*$-tree reduces the number of cache miss occurrences by loading the reduced internal nodes that do not have index entries. And it supports the sequential access of index entries for range query by connecting adjacent terminal nodes and internal index nodes. For performance evaluation, we have developed a cost model, and compared our $CST^*$-tree with existing CST-tree, that is the conventional cache sensitive T-tree, and $T^*$-tree, that is conventional the range query search T -tree, by using the cost model. The results indicate that cache miss occurrence of $CST^*$-tree is decreased by 20~30% over that of CST-tree in a single value search, and it is decreased by 10~20% over that of $T^*$-tree in a range query search.

• The Transactions of the Korea Information Processing Society
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• v.4 no.3
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• pp.709-719
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• 1997

Performance of an algorithm can be mesaurde from serval aspects.Suppose thre is a query formulation al-gorithm.Even though this algorithm shows high retrival performance, ie, high recall and percision, retriveing items can rake a long time.In this study, we time complexity of automatic query reformulation algorithms, named the query Tree, DNF method, and Dillon's method, and comparethem in theoretical and practical aspects using a tral-time performance)the absolute times for each algorithm to fromulate a query)in a Sun SparcStation 2. In experiments using three test sets, CSCM, CISI, and Medlars, the query Tree algorithm was the fastest among the three algorithms tested.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.1
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• pp.39-46
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• 2011

The present study introduces a comparison between 3D R-tree and octree which are noticeable candidates to index large point clouds gathered from a 3D terrestrial laser scanner. A query method, which is to find neighboring points within given distances, was devised for the comparison, and time lapses for the query along with memory usages were checked. From tests conducted on point clouds scanned from a building and a stone pagoda, it was shown that octree has the advantage of fast generation and query while 3D R-tree is more memory-efficient. Both index and leaf capacity were revealed to be ruling factors to get the best performance of 3D R-tree, while the number of level was of oetree.

• 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 Journal of Information Technology and Database
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• v.7 no.2
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• pp.17-32
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• 2000

As XML emerging as the Internet electronic document language standard of the next generation, the number of XML documents which contain vast amount of Information is increasing substantially through the transformation of existing documents to XML documents or the appearance of new XML documents. Consequently, XML document retrieval system becomes extremely essential for searching through a large quantity of XML documents that are storied in and managed by DBMS. In this paper we describe the design and implementation of BADA-IV/XML query processor that supports content-based, structure-based and attribute-based retrieval. We design XML query language based upon XQL (XML Query Language) of W3C and tightly-coupled with OQL (a query language for object-oriented database). XML document is stored and maintained in BADA-IV, which is an object-oriented database management system developed by ETRI (Electronics and Telecommunications Research Institute) The storage data model is based on DOM (Document Object Model), therefore the retrieval of XML documents is executed basically using DOM tree traversal. We improve the search performance using Node ID which represents node's hierarchy information in an XML document. Assuming that DOW tree is a complete k-ary tree, we show that Node ID technique is superior to DOM tree traversal from the viewpoint of node fetch counts.

• Journal of KIISE:Databases
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• v.36 no.3
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• pp.226-234
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• 2009

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Due to the characteristics of a data stream, it is impossible to save all the data elements of a data stream. Therefore it is necessary to define a new synopsis structure to store the summary information of a data stream. For this purpose, this paper proposes a cuboid prefix tree that can be effectively employed in evaluating an iceberg query over data streams. A cuboid prefix tree only stores those itemsets that consist of grouping attributes used in GROUP BY query. In addition, a cuboid prefix tree can compute multiple iceberg queries simultaneously by sharing their common sub-expressions. A cuboid prefix tree evaluates an iceberg query over an infinitely generated data stream while efficiently reducing memory usage and processing time, which is verified by a series of experiments.

• Journal of Korea Multimedia Society
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• v.14 no.5
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• pp.669-680
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• 2011

R-trees are widely used in various areas such as geographical information systems, CAD systems and spatial databases in order to efficiently index multi-dimensional data. As data sets used in these areas grow in size and complexity, however, range query operations on R-tree are needed to be further faster to meet the area-specific constraints. To address this problem, there have been various research efforts to develop strategies for acceleration query processing on R-tree by using the buffer mechanism or parallelizing the query processing on R-tree through multiple disks and processors. As a part of the strategies, approaches which parallelize query processing on R-tree through Graphics Processor Units(GPUs) have been explored. The use of GPUs may guarantee improved performances resulting from faster calculations and reduced disk accesses but may cause additional overhead costs caused by high memory access latencies and low data exchange rate between GPUs and the CPU. In this paper, to address the overhead problems and to adapt GPUs efficiently, we propose a novel approach which uses a GPU as a buffer to parallelize query processing on R-tree. The use of buffer algorithm can give improved performance by reducing the number of disk access and maximizing coalesced memory access resulting in minimizing GPU memory access latencies. Through the extensive performance studies, we observed that the proposed approach achieved up to 5 times higher query performance than the original CPU-based R-trees.

• Journal of KIISE:Databases
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• v.30 no.5
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• pp.521-531
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• 2003

Multi-level geographic data can be mainpulated by a window query such as a zoom operation. In order to handle multi-level geographic data efficiently, a spatial indexing method supporting a window query is needed. However, the conventional spatial indexing methods are not efficient to access multi-level geographic data quickly. To solve it, other a few spatial indexing methods for multi-level geographic data are known. However these methods do not support all types of multi-level geographic data. This paper presents a new efficient spatial indexing method, the MLR-tree for window query of multi-level geographic data. The MLR-tree offers both high search performance and no data redundancy. Experiments show them. Moreover, the MLR-tree supports all types of multi-level geographic data.