• 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.

• The KIPS Transactions:PartB
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• v.12B no.4 s.100
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• pp.487-498
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• 2005

Recently, It has focused on decision tree algorithm that can handle large dataset. However, because most of these algorithms for large datasets process data in a batch mode, if new data is added, they have to rebuild the tree from scratch. h more efficient approach to reducing the cost problem of rebuilding is an approach that builds a tree incrementally. Representative algorithms for incremental tree construction methods are BOAT and ITI and most of these algorithms use a local discretization method to handle the numeric data type. However, because a discretization requires sorted numeric data in situation of processing large data sets, a global discretization method that sorts all data only once is more suitable than a local discretization method that sorts in every node. This paper proposes an incremental tree construction method that efficiently rebuilds a tree using a global discretization method to handle the numeric data type. When new data is added, new categories influenced by the data should be recreated, and then the tree structure should be changed in accordance with category changes. This paper proposes a method that extracts sample points and performs discretiration from these sample points to recreate categories efficiently and uses confidence intervals and a tree restructuring method to adjust tree structure to category changes. In this study, an experiment using people database was made to compare the proposed method with the existing one that uses a local discretization.

• Spatial Information Research
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• v.7 no.2
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• pp.271-282
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• 1999

In this paper, we derived an efficient indexing scheme, SJ tree, which handles multi-attribute data and spatial join operations efficiently. In addition, a number of algorithms for manipulating multi-attribute data are given , together with their computational and I/O complexity . Moreover , we how that SJ tree is a kind of generalized B-tree. This means that SJ-tree can be easily implemented on existing built-in B-tree in most storage managers in the sense that the structure of SJ tree is like that of B-tree. The spatial join operation with spatial output is benchmarked using R-tree, B-tree, K-D-B tree, and SJ tree. Results from the benchmark test indicate that SJ tree out performance other indexing schemes on spatial join with point data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.12
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• pp.3219-3236
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• 2012

A tree routing structure is often adopted for many-to-one data gathering and aggregation in sensor networks. For real-time scenarios, considering lossy wireless links, it is an important issue how to construct a maximum-lifetime data gathering tree with delay constraint. In this work, we study the problem of lifetime-preserving and delay-constrained tree construction in unreliable sensor networks. We prove that the problem is NP-complete. A greedy approximation algorithm is proposed. We use expected transmissions count (ETX) as the link quality indicator, as well as a measure of delay. Our algorithm starts from an arbitrary least ETX tree, and iteratively adjusts the hierarchy of the tree to reduce the load on bottleneck nodes by pruning and grafting its sub-tree. The complexity of the proposed algorithm is $O(N^4)$. Finally, extensive simulations are carried out to verify our approach. Simulation results show that our algorithm provides longer lifetime in various situations compared to existing data gathering schemes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.4
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• pp.753-758
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• 2017

The ray-shooting problem is to find the first intersection point on the surface of given geometric objects where a ray moving along a straight line hits. Since rays are usually given in the form of queries, this problem is typically solved as follows. First, a data structure for a collection of objects is constructed as preprocessing. Then, the answer for each query ray is quickly computed using the data structure. In this paper, we consider the ray-shooting problem about the set of vertical line segments on the x-axis. We present a new data structure called a convex layer tree for n vertical line segments given by input. This is a tree structure consisting of layers of convex hulls of vertical line segments. It can be constructed in O(n log n) time and O(n) space and is easy to implement. We also present an algorithm to solve each query in O(log n) time using this data structure.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.1
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• pp.39-45
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• 2014

This paper proposes an architecture of a sensor network for gathering data under a powerful beam cluster tree architecture. This architecture is used when there is a need to gather data from sensor node where there is no sink node connected to an existing network, or it is required to get a series of data specific to an event or time. The transmit distance of the beam signal is longer than that of the usual sensor node. The nodes of the network make a tree network when receiving a beam message transmitting from the powerful root node. All sensor nodes in a sink tree network synchronize to the superframe and know exactly the sequence value of the current superframe. When there is data to send to the sink node, the sensor node sends data at the corresponding allocated channel. Data sending schemes under the guaranteed time slot are tested and the delay and jitter performance is explained.

• Journal of Information Processing Systems
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• v.6 no.1
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• pp.79-90
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• 2010

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. The continuous characteristic of streaming data necessitates the use of algorithms that require only one scan over the stream for knowledge discovery. Data mining over data streams should support the flexible trade-off between processing time and mining accuracy. In many application areas, mining frequent itemsets has been suggested to find important frequent itemsets by considering the weight of itemsets. In this paper, we present an efficient algorithm WSFI (Weighted Support Frequent Itemsets)-Mine with normalized weight over data streams. Moreover, we propose a novel tree structure, called the Weighted Support FP-Tree (WSFP-Tree), that stores compressed crucial information about frequent itemsets. Empirical results show that our algorithm outperforms comparative algorithms under the windowed streaming model.

• Journal of the Korea Society for Simulation
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• v.4 no.2
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• pp.61-68
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• 1995

The performance of event-driven logic simulation frequently used for VLSI design verification depends on the data structures for event queues. This paper improves the existing Timing Wheel as a data structure for an event queue. In case of the use of B+ tree, an efficient node degree is also presented based on the experiment results. A new Timing Wheel index structure, which eliminates the insertion and deletion overhead of B+ tree, is proposed and analyzed.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.3
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• pp.155-164
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• 2016

For frequency pattern analysis of large databases, the new tree-based frequency pattern analysis algorithm which can compensate for the disadvantages of the Apriori method has been variously studied. In frequency pattern tree, the number of nodes is associated with memory allocation, but also affects memory resource consumption and processing speed of the growth. Therefore, reducing the number of nodes in the tree is very important in the frequency pattern mining. However, the absolute criteria which need to order the transaction items for construction frequency pattern tree has lowered the compression ratio of the tree nodes. But most of the frequency based tree construction methods adapted the absolute criteria. FP-tree is typically frequency pattern tree structure which is an extended prefix-tree structure for storing compressed frequent crucial information about frequent patterns. For construction the tree, all the frequent items in different transactions are sorted according to the absolute criteria, frequency descending order. CanTree also need to absolute criteria, canonical order, to construct the tree. In this paper, we proposed a novel frequency pattern tree construction method that does not use the absolute criteria, IRFP-tree algorithm. IRFP-tree(Intersection Rule based FP-tree). IRFP-tree is constituted with the new paradigm of the intersection rule without the use of the absolute criteria. It increased the compression ratio of the tree nodes, and reduced the tree construction time. Our method has the additional advantage that it provides incremental mining. The reported test result demonstrate the applicability and effectiveness of the proposed approach.

• Journal of Korea Multimedia Society
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• v.15 no.10
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• pp.1257-1263
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• 2012

An implementation of animation module using the 3D body data scanned by laser scanner is reported in this paper. Characteristic points of the skeleton in human body were picked up as pivot point for 3D rotation. The body data set wes reconstructed as objects built in hierarchical tree structure, which is based on skeleton model. In order to implement the 3D animation of the laser scanned body data, the vertexes of the objects were connected as skeleton structure and animated to follow dynamic patterns inputted by user.