• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.7
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• pp.925-934
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• 2017

R-tree is index structure which is frequently used for handling spatial data. However, if the number of dimensions increases, or if only partial dimensions are used for searching the certain data according to user preference, the time for indexing is greatly increased and the efficiency of the generated R-tree is greatly reduced. Hence, it is not suitable for the multidimensional data, where dimensions are continuously increasing. In this paper, we propose a multidimensional hash index, a new multidimensional index structure based on a hash index. The multidimensional hash index classifies data into buckets of euclidean space through a hash function, and then, when an actual search is requested, generates a hash search tree for effective searching. The generated hash search tree is able to handle user preferences in selected dimensional space. Experimental results show that the proposed method has better indexing performance than R-tree, while maintaining the similar search performance.

• Journal of Korea Spatial Information System Society
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• v.11 no.3
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• pp.55-62
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• 2009

Location based Services in the ubiquitous computing environment, namely u-LBS, use very large and skewed spatial objects that are closely related to locational information. It is especially essential to achieve fast search, which is looking for POI(Point of Interest) related to the location of users. This paper examines how to search large and skewed POI efficiently in the u-LBS environment. We propose the Dynamic-level Grid based R-Tree(DGR-Tree), which is an index for point data that can reduce the cost of stationary POI search. DGR-Tree uses both R-Tree as a primary index and Dynamic-level Grid as a secondary index. DGR-Tree is optimized to be suitable for point data and solves the overlapping problem among leaf nodes. Dynamic-level Grid of DGR-Tree is created dynamically according to the density of POI. Each cell in Dynamic-level Grid has a leaf node pointer for direct access with the leaf node of the primary index. Therefore, the index access performance is improved greatly by accessing the leaf node directly through Dynamic-level Grid. We also propose a K-Nearest Neighbor(KNN) algorithm for DGR-Tree, which utilizes Dynamic-level Grid for fast access to candidate cells. The KNN algorithm for DGR-Tree provides the mechanism, which can access directly to cells enclosing given query point and adjacent cells without tree traversal. The KNN algorithm minimizes sorting cost about candidate lists with minimum distance and provides NEB(Non Extensible Boundary), which need not consider the extension of candidate nodes for KNN search.

• 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 KIPS Transactions:PartD
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• v.8D no.4
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• pp.305-312
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• 2001

Recently, high-dimensional index structures have been required for similarity search in such database applications s multimedia database and data warehousing. In this paper, we propose a new cell-based signature tree, called CS-tree, which supports efficient storage and retrieval on high-dimensional feature vectors. The proposed CS-tree partitions a high-dimensional feature space into a group of cells and represents a feature vector as its corresponding cell signature. By using cell signatures rather than real feature vectors, it is possible to reduce the height of our CS-tree, leading to efficient retrieval performance. In addition, we present a similarity search algorithm for efficiently pruning the search space based on cells. Finally, we compare the performance of our CS-tree with that of the X-tree being considered as an efficient high-dimensional index structure, in terms of insertion time, retrieval time for a k-nearest neighbor query, and storage overhead. It is shown from experimental results that our CS-tree is better on retrieval performance than the X-tree.

• Journal of KIISE:Databases
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• v.36 no.1
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• pp.44-49
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• 2009

In recent years, with the development of portable terminals, various searching services on large data have been provided in portable terminals. In order to search large data, most applications for information retrieval use indexes such as B-trees or R-trees. However, only a small portion of the data set is accessed by users, and the access frequencies of each data are not uniform. The existing indexes such as B-trees or R-trees do not consider the properties of the skewed access patterns. And a cache stores the frequently accessed data for fast access in memory. But the size of memory used in the cache is restricted. In this paper, we propose a new index based on disk, called J-tree, which considers user's search patterns. The proposed index is a balanced tree which guarantees uniform searching time on all data. It also supports fast searching time on the frequently accessed data. Our experiments show the effectiveness of our proposed index under various settings.

• Journal of Korean Society for Geospatial Information Science
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• v.3 no.2 s.6
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• pp.137-149
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• 1995

A spatial index is necessary to support an efficient search in a geographical information system (GIS) that is important in these days. In this paper, we design and implement a geographical data manager, called GOOD (Geo-object Oriented Data Manager) 2.0, by extending GOOD 1.0 with a spatial index processing module. That is, R-tree and R*-tree are used as a spatial index in this paper to make an efficient search possible. In addition, this paper conducts a performance evaluation to measure the improvement in search efficiency and analyzes the results of the performance evaluation. When the performance evaluation is carried out, we consider various environment factors to allow an GIS administrator to use the results as a basic data in selecting an appropriate spatial index.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.5
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• pp.527-547
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• 2009

Moving object management is widely used in traffic, logistic and data mining applications in ubiquitous environments. It is required to analyze spatio-temporal data and trajectories for moving object management. In this paper, we proposed a novel index structure for spatio-temporal aggregation of trajectory in a constrained network, named aCN-RB-tree. It manages aggregation values of trajectories using a constraint network-based index and it also supports direction of trajectory. An aCN-RB-tree consists of an aR-tree in its center and an extended B-tree. In this structure, an aR-tree is similar to a Min/Max R-tree, which stores the child nodes' max aggregation value in the parent node. Also, the proposed index structure is based on a constrained network structure such as a FNR-tree, so that it can decrease the dead space of index nodes. Each leaf node of an aR-tree has an extended B-tree which can store timestamp-based aggregation values. As it considers the direction of trajectory, the extended B-tree has a structure with direction. So this kind of aCN-RB-tree index can support efficient search for trajectory and traffic zone. The aCN-RB-tree can find a moving object trajectory in a given time interval efficiently. It can support traffic management systems and mining systems in ubiquitous environments.

• Journal of KIISE:Databases
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• v.35 no.1
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• pp.17-28
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• 2008

Recently, main memory access is a performance bottleneck for many computer applications. Cache memory is introduced in order to reduce memory access latency. However, it is possible for cache memory to reduce memory access latency, when desired data are located on cache. EST tree is proposed to solve this problem by improving T tree. However, when doing a range search, EST tree has to search unnecessary nodes. Therefore, this paper proposes $CST^+$ tree which has the merit of CST tree and is possible to do a range search by linking data nodes with linked lists. By experiments, we show that $CST^+$ is $4{\sim}10$ times as fast as CST and $CSB^+$. In addition, rebuilding an index Is an essential step for the database recovery from system failure. In this paper, we propose a fast tree index rebuilding algorithm called MaxPL. MaxPL has no node-split overhead and employs a parallelism for reading the data records and inserting the keys into the index. We show that MaxPL is $2{\sim}11$ times as fast as sequential insert and batch insert.

• Journal of Communications and Networks
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• v.18 no.5
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• pp.699-712
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• 2016

Recently, greedy algorithm has received much attention as a cost-effective means to reconstruct the sparse signals from compressed measurements. Much of previous work has focused on the investigation of a single candidate to identify the support (index set of nonzero elements) of the sparse signals. Well-known drawback of the greedy approach is that the chosen candidate is often not the optimal solution due to the myopic decision in each iteration. In this paper, we propose a tree search based sparse signal recovery algorithm referred to as the tree search matching pursuit (TSMP). Two key ingredients of the proposed TSMP algorithm to control the computational complexity are the pre-selection to put a restriction on columns of the sensing matrix to be investigated and the tree pruning to eliminate unpromising paths from the search tree. In numerical simulations of Internet of Things (IoT) environments, it is shown that TSMP outperforms conventional schemes by a large margin.

• Journal of the Korea Society of Computer and Information
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• v.14 no.7
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• pp.89-95
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• 2009

This paper proposes a tag indexing scheme for RFID tag using spatial index. The tag being used for the inventory management and the tag's location is determined by the position of readers. Therefore, the reader recognizes the tag, which is attached products and thereby their positions can be traced down. In this paper, we propose hTag-tree( Hybrid Tag index) which manages RFID tag attached products. hTag-tree is a new index, which is based on tag's attributes with fast searching, and this tag index manages RFID tags using reader's location. This tag index accesses rapidly to tags for insertion, deletion and updating in dynamic environment. This can minimize the number of node accesses in tag searching comparing to previous techniques. Also, by the extension of MER in present tag index, it is helpful to stop the lowering of capacity which can be caused by parent node approach. The proposed index experiment deals with the comparison of tag index. Fixed Interval R-tree, and present spatial index, R-tree comparison. As a result, the amount of searching time is significantly shortened through hTag-tree node access in data search. This shows that the use of proposed index improves the capacity of effective management of a large amount of RFID tag.