• 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 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.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.258-261
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• 2007

Although there have been lack of studies using X-band SAR data particularly for forestry application as compared to C-, and L-band SAR data, it has a potential to distinguish tree species because most signals are backscattered on the top of canopy. This study aimed to compare signal characteristics of multi-band SAR data including X-band for classifying tree species. The data used for the study are SIR-C/X-SAR data (X-, C-, L-band) obtained on Oct. 3, 1994 over the forest area near Seoul, S. Korea. Thirty ground sample plots were collected per each tree species. Initial comparison of backscattering coefficients among three SAR bands shows that X-band data showed better separation of tree species than C- and L-band SAR data irrespective of polarization. The weak penetrating in canopy layer might be possible source of information for X-band data to be useful for the classification of forest species and cover type mapping.

• 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.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.396-417
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• 2018

GR-tree and query aggregation techniques have been proposed for spatial query processing in conventional spatial query processing for wireless sensor networks. Although these spatial query processing techniques consider spatial query optimization, time query optimization is not taken into consideration. The index reorganization cost and communication cost for the parent sensor nodes increase the energy consumption that is required to ensure the most efficient operation in the wireless sensor node. This paper proposes itinerary-based R-tree (IR-tree) for more efficient spatial-temporal query processing in wireless sensor networks. This paper analyzes the performance of previous studies and IR-tree, which are the conventional spatial query processing techniques, with regard to the accuracy, energy consumption, and query processing time of the query results using the wireless sensor data with Uniform, Gauss, and Skew distributions. This paper proves the superiority of the proposed IR-tree-based space-time indexing.

• International Journal of Reliability and Applications
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• v.3 no.2
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• pp.61-80
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• 2002

Tree-harvesting worker data of 508 separate worker accidents are analyzed and an exploratory approach taken. The worker accident data cover a sample of five years. The scope of the study was the southeastern United States of America. As might be hypothesized, the chainsaw was the most hazardous type of tree-harvesting equipment. It accounted for 55％ of the tree-harvesting accidents. Most chainsaw accidents resulted in injuries to the lower extremities and were more frequent among younger employees. The probability of one or more chainsaw accidents occurring in any 30-day period was approximately 0.856. Chainsaw accidents were more likely to occur in late morning and early afternoon. We used statistical tools such as Pareto charts, c-charts and Ishikawa diagrams. Such tools are useful in diagnosing the root-cause of tree-harvesting worker accidents and help in developing preventive safety programs. Recommendations to help improve the quality of information of accident data collected by insurance companies and others are briefly given. The strategy and culture of continuous process improvements are stressed.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.2
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• pp.79-86
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• 2019

For decades, in-memory data structures have been designed for DRAM-based main memory that provides symmetric read/write performances and has no limited write endurance. However, such data structures provide sub-optimal performance for NVM as it has different characteristics to DRAM. With this motivation, we rethink a conventional red-black tree in terms of its efficacy under NVM settings. The original red-black tree constantly rebalances sub-trees so as to export fast access time over dataset, but it inevitably increases the write traffic, adversely affecting the performance for NVM with a long write latency and limited endurance. To resolve this problem, we present a variant of the red-black tree called a hierarchical balanced binary search tree. The proposed structure maintains multiple keys in a single node so as to amortize the rebalancing cost. The performance study reveals that the proposed hierarchical binary search tree effectively reduces the write traffic by effectively reaping the high capacity of NVM.

• Management Science and Financial Engineering
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• v.3 no.1
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• pp.39-56
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• 1997

This paper applies the binary tree classifier and discriminant analysis methods to predicting failures of banks and insurance companies. In this study, discriminant analysis is generally better than the binary tree classifier in the classification of bank defaults; the binary tree is generally better than discriminant analysis in the classification of insurance company defaults. This situation can be explained that the performance of a classifier depends on the characteristics of the data. If the data are dispersed appropriately for the classifier, the classifier will show a good performance. Otherwise, it may show a poor performance. The two data sets (bank and insurance) are analyzed to explain the better performance of the binary tree in insurance and the worse performance in bank; the better performance of discriminant analysis in bank and the worse performance in insurance.

• Journal of the Korean Data and Information Science Society
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• v.27 no.1
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• pp.255-264
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• 2016

Classification is a predictive modeling for a categorical target variable. Various classification ensemble methods, which predict with better accuracy by combining multiple classifiers, became a powerful machine learning and data mining paradigm. Well-known methodologies of classification ensemble are boosting, bagging and random forest. In this article, we assume that decision trees are used as classifiers in the ensemble. Further, we hypothesized that tree size affects classification accuracy. To study how the tree size in uences accuracy, we performed experiments using twenty-eight data sets. Then we compare the performances of ensemble algorithms; bagging, double-bagging, boosting and random forest, with different tree sizes in the experiment.