• The Journal of the Korea Contents Association
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• v.4 no.4
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• pp.141-152
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• 2004

In this paper, we propose the index structure supporting the future position retrievals with efficiently updating continuous positions of moving objects in location based services. For reducing update costs of moving objects, our index structure directly accesses to the leaf node with moving objects using secondary index structure and performs bottom up update when node information is changed. Positions of moving objects are stored in primary index structure. In primary index structure, the split information similar to kd-tree is stored to internal node for increasing node's fanout. And the proposed index structure supports the future position retrievals using velocity of moving objects in the child node.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.157-159
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• 2007

The conventional hard disk has been the dominant database storage system for over 25 years. Recently, hybrid systems which incorporate the advantages of flash memory into the conventional hard disks are considered to be the next dominant storage systems to support databases for desktops and server computers. Their features are satisfying the requirements like enhanced data I/O, energy consumption and reduced boot time, and they are sufficient to hybrid storage systems as major database storages. However, we need to improve traditional index node management schemes based on B-Tree due to the relatively slow characteristics of hard disk operations, as compared to flash memory. In order to achieve this goal, we propose a new index node management scheme called FNC-Tree. FNC-Tree-based index node management enhanced search and update performance by caching data objects in unused free area of flash leaf nodes to reduce slow hard disk I/Os in index access processes.

• Journal of KIISE:Databases
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• v.33 no.5
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• pp.495-512
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• 2006

Recently, researches on a future position prediction of moving objects have been progressed as the importance of the future position retrieval increases. New index structures are required to efficiently retrieve the consecutive positions of moving objects. Existing index structures significantly degrade the search performance of the moving objects because the search operation makes the unnecessary extension of the node in the index structure. To solve this problem, we propose a space partition based index structure considering the mobility of moving objects. To deal with the overflow of a node, our index structure first merges it and the sibling node. If it is impossible to merge them, our method splits the overflow node in which moving properties of objects are considered. Our index structure is always partitioned into overlap free subregions when a node is split. Our split strategy chooses the split position by considering the parameters such as velocities, the escape time of the objects, and the update time of a node. In the internal node, the split position Is determined from preventing the cascading split of the child node. We perform various experiments to show that our index structure outperforms the existing index structures in terms of retrieval performance. Our experimental results show that our proposed index structure achieves about $17%{\sim}264%$ performance gains on current position retrieval and about $107%{\sim}19l%$ on future position retrieval over the existing methods.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.673-676
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• 2004

To manage and query moving objects efficiently in MMDBMS, a memory index structure should be used. The most popular index structure for storing trajectories of moving objects is 3DR-tree. The 3DR-tree also can be used for MMDBMS. However, the volume of data can exceed the capacity of physical memory since moving objects report their locations continuously. To accommodate new location reports, old trajectories should be migrated to disk or purged from memory. This paper focuses on migration policies of a main memory index structure. Migration policies consist of two steps: (i) node selection, (ii) node placement. The first step (node selection) selects nodes that should be migrated to disk. The criteria of selection are the performance of insertion or query. The second step (node placement) determines the order of nodes written to disk. This step can be thought as dynamic declustering policies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.691-694
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• 2010

Since the data collected at a sensor node is the stream data, for processing efficiently user queries, the query index should be constructed at each node. To construct the minimized query index at the node, it is required to reduce the number of query constraints inserted into the query index. In this paper, we propose the scheme of the query constraints distribution using the multi-dimensional data index in order to diminish the number of the inserted query constraints.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.05a
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• pp.51-54
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• 2005

This paper proposes an index structure which is used to process X-Path on S-XML data. There are many previous index structures based on tree structure for X-Path processing. Because of general tree index's top-down query fashion, the unnecessary node traversal makes heavy access and decreases the query processing performance. And both of the two query types for X-Path called single-path query and branching query need to be supported in proposed index structure. This method uses a combination of path summary and the node indexing. First, it manages hashing on hierarchy elements which are presented in tag in S-XML. Second, array blocks named path summary array is created in each node of hashing to store the path information. The X-Path processing finds the tag element using hashing and checks array blocks in each node to determine the path of query's result. Based on this structure, it supports both single-path query and branching path query and improves the X-Path processing performance.

• The KIPS Transactions:PartD
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• v.18D no.4
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• pp.225-236
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• 2011

Various applications of LBS (Location Based Services) are being developed to provide the customized service depending on user's location with progress of wireless communication technology and miniaturization of personalized device. To effectively process an amount of vehicles' location data, LBS requires the techniques such as vehicle observation, data communication, data insertion and search, and user query processing. In this paper, we propose the historical location index, GIP-FB (Group Insertion tree with Flexible Buffer Node) and the flexible buffer node technique to adjust the cost of data insertion and search. the designed GIP+ based index employs the buffer node and the projection storage to cut the cost of insertion and search. Besides, it adjusts the cost of insertion and search by changing the number of line segments of the buffer node with user defined time interval. In the experiment, the buffer node size influences the performance of GIP-FB by changing the number of non-leaf node of the index. the proposed flexible buffer node is used to adjust the performance of the historical location index depending on the applications of LBS.

• Journal of KIISE:Databases
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• v.32 no.5
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• pp.509-525
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• 2005

In this paper, we propose the index structure based on a vector approximation for efficiently supporting the similarity search of multi-dimensional data. The proposed index structure splits a region with the space partition method and allocates to the split region dynamic bits according to the distribution of data. Therefore, the index structure splits a region to the unoverlapped regions and can reduce the depth of the tree by storing the much region information of child nodes in a internal node. Our index structure represents the child node more exactly and provide the efficient search by representing the region information of the child node relatively using the region information of the parent node. We show that our proposed index structure is better than the existing index structure in various experiments. Experimental results show that our proposed index structure achieves about $40\%$ performance improvements on search performance over the existing method.

• International Journal of Contents
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• v.3 no.1
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• pp.19-23
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• 2007

Recently, several main-memory index structures have been proposed to reduce the impact of secondary cache misses. In mainmemory storage systems, secondary cache misses have a substantial effect on the performance of index structures. However, recent studies still stiffer from secondary cache misses when visiting each level of index tree. In this paper, we propose a new index structure that minimizes the total amount of cache miss latency. The proposed index structure prefetched grandchildren of a current node. The basic structure of the proposed index structure is based on that of the CSB+-Tree, which uses the concept of a node group to increase fan-out. However, the insert algorithm of the proposed index structure significantly reduces the cost of a split. The superiority of our algorithm is shown through performance evaluation.

• Journal of the Korean Physical Society
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• v.73 no.10
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• pp.1589-1595
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• 2018

Similarity index measures the topological proximity of node pairs in a complex network. Numerous similarity indices have been defined and investigated, but the dependency of structure on the performance of similarity indices has not been sufficiently investigated. In this study, we investigated the relationship between the performance of similarity indices and structural properties of a network by employing a two-state random network. A node in a two-state network has binary types that are initially given, and a connection probability is determined from the state of the node pair. The performances of similarity indices are affected by the number of links and the ratio of intra-connections to inter-connections. Similarity indices have different characteristics depending on their type. Local indices perform well in small-size networks and do not depend on whether the structure is intra-dominant or inter-dominant. In contrast, global indices perform better in large-size networks, and some such indices do not perform well in an inter-dominant structure. We also found that link prediction performance and the performance of similarity are correlated in both model networks and empirical networks. This relationship implies that link prediction performance can be used as an approximation for the performance of the similarity index when information about node type is unavailable. This relationship may help to find the appropriate index for given networks.