• Journal of KIISE:Databases
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• v.36 no.1
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• pp.50-62
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• 2009

In wireless and mobile environments, data broadcasting is recognized as an effective way for data dissemination due to its benefits to bandwidth efficiency, energy-efficiency, and scalability. In this paper, we address the problem of delayed query processing raised by tree-based index structures in wireless broadcast environments, which increases the access time of the mobile clients. We propose a novel distributed index structure and a clustering strategy for streaming XML data which enable energy and latency-efficient broadcast of XML data. We first define the DIX node structure to implement a fully distributed index structure which contains tag name, attributes, and text content of an element as well as its corresponding indices. By exploiting the index information in the DIX node stream, a mobile client can access the wireless stream in a shorter latency. We also suggest a method of clustering DIX nodes in the stream, which can further enhance the performance of query processing over the stream in the mobile clients. Through extensive performance experiments, we demonstrate that our approach is effective for wireless broadcasting of XML data and outperforms the previous methods.

• Journal of Korea Multimedia Society
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• v.10 no.9
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• pp.1093-1105
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• 2007

This paper presents a multidimensional type inheritance indexing technique (MD-TIX) for XML databases. We use a multidimensional file organization as the index structure. In conventional XML database indexing techniques using one-dimensional index structures, they do not efficiently handle complex queries involving both nested elements and type inheritance hierarchies. We extend a two-dimensional type hierarchy indexing technique(2D-THI) for indexing the nested elements of XML databases. 2D-THI is an indexing scheme that deals with the problem of clustering elements in a two-dimensional domain space consisting of the key value domain and the type identifier domain for indexing a simple element in a type hierarchy. In our extended scheme, we handle the clustering of the index entries in a multidimensional domain space consisting of a key value domain and multiple type identifier domains that include one type identifier domain per type hierarchy on a path expression. This scheme efficiently supports queries that involve search conditions on the nested element represented by an extended path expression. An extended path expression is a path expression in which every type hierarchy on a path can be substituted by an individual type or a subtype hierarchy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5692-5716
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• 2019

One of the best means to safeguard the confidentiality, security, and privacy of an image within the IoT-Cloud is through encryption. However, looking through encrypted data is a difficult process. Several techniques for searching encrypted data have been devised, but certain security solutions may not be used in IoT-Cloud because such solutions are not lightweight. We propose a lightweight scheme that can perform a content-based search of encrypted images, namely EEIRI. In this scheme, the images are represented using local features. We develop and validate a secure scheme for measuring the Euclidean distance between two descriptor sets. To improve the search efficiency, we employ the k-means clustering technique to construct a searchable tree-based index. Our index construction process ensures the privacy of the stored data and search requests. When compared with more familiar techniques of searching images over plaintexts, EEIRI is considered to be more efficient, demonstrating a higher search cost of 7% and a decrease in search accuracy of 1.7%. Numerous empirical investigations are carried out in relation to real image collections so as to evidence our work.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.3
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• pp.273-283
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• 2000

In this paper we suggest an optimal design method of Fuzzy-Neural Networks(FNN) model for complex and nonlinear systems. The FNNs use the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM(Hard C-Means) Clustering Algorithm to find initial parameters of the membership function. The parameters such as parameters of membership functions learning rates and momentum weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. According to selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity (distribution of I/O data we show that it is available and effective to design and optimal FNN model structure with a mutual balance and dependency between approximation and generalization abilities. This methodology sheds light on the role and impact of different parameters of the model on its performance (especially the mapping and predicting capabilities of the rule based computing). To evaluate the performance of the proposed model we use the time series data for gas furnace the data of sewage treatment process and traffic route choice process.

• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.349-356
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• 2014

In baseball, sabermetric batting statistics are used to compare an offensive performance of players. There exist dozens of sabermetric statistics, but baseball fans don't like the complexity of an abundance of measures. This paper provides a batting grade index (BGI) using principal component based on eight batting statistics. These are OPS, ISO, SECA, TA, RC, RC/27, wOBA and XR. We show that how standardized batting statistics are aggregated and weighted to arrive at a single composite measure of BGI. Also our result allows for segmentation of players into groups using the K-means clustering algorithm.

• Journal of Internet Computing and Services
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• v.8 no.5
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• pp.133-140
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• 2007

Currently, as the requirement for high quality Internet access from anywhere at anytime is consistently increasing, the interconnection of pure ad hoc networks to fixed IP networks becomes increasingly important. Such integrated network, referred to as hybrid ad hoc networks, can be extended to many applications, including Sensor Networks, Home Networks, Telematics, and so on. We focus on some data communication problems of hybrid ad hoc networks, such as broadcasting and routing. In particular. power failure of mobile terminals is the most important factor since it affects the overall network lifetime. We propose an energy-efficient routing protocol based on clustering for hybrid ad hoc networks. By applying the index-based data broadcasting and selective tuning methods, the infra system performs the major operations related to clustering and routing on behalf of ad hoc nodes. The proposed scheme reduces power consumption as well as the cost of path discovery and maintenance, and the delay required to configure the route.

• Journal of KIISE:Software and Applications
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• v.30 no.1_2
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• pp.129-139
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• 2003

The PC cluster architecture is considered as a cost-effective alternative to the existing supercomputers for realizing a high-performance information retrieval (IR) system. To implement an efficient IR system on a PC cluster, it is essential to achieve maximum parallelism by having the data appropriately distributed to the local hard disks of the PCs in such a way that the disk I/O and the subsequent computation are distributed as evenly as possible to all the PCs. If the terms in the inverted index file can be classified to closely related clusters, the parallelism can be maximized by distributing them to the PCs in an interleaved manner. One of the goals of this research is the development of methods for automatically clustering the terms based on the likelihood of the terms' co-occurrence in the same query. Also, in this paper, we propose a method for duplicate distribution of inverted index records among the PCs to achieve fault-tolerance as well as dynamic load balancing. Experiments with a large corpus revealed the efficiency and effectiveness of our method.

• Journal of KIISE:Software and Applications
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• v.31 no.7
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• pp.911-921
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• 2004

Accurate minutiae extraction from input fingerprint images is one of the critical modules in robust automatic fingerprint identification system. However, the performance of a minutiae extraction is heavily dependent on the quality of the input fingerprint images. If the preprocessing is performed according to the fingerprint image characteristics in the image enhancement step, the system performance will be more robust. In this paper, we propose a knowledge-based preprocessing method, which extracts S features (the mean and variance of gray values, block directional difference, orientation change level, and ridge-valley thickness ratio) from the fingerprint images and analyzes image quality with Ward's clustering algorithm, and enhances the images with respect to oily/neutral/dry characteristics. Experimental results using NIST DB 4 and Inha University DB show that clustering algorithm distinguishes the image Quality characteristics well. In addition, the performance of the proposed method is assessed using quality index and block directional difference. The results indicate that the proposed method improves both the quality index and block directional difference.

• Journal of Information Technology and Architecture
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• v.10 no.1
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• pp.123-135
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• 2013

This paper presents a two-dimensional grouping index(2DG-index) for efficient processing of XML filtering queries. Recently, many index techniques have been suggested for the efficient processing of structural relationships among the elements in the XML database such as an ancestor- descendant and a parent-child relationship. However, these index techniques focus on simple path queries, and don't consider the path queries that include a condition value for filtering. The 2DG-index is an index structure that deals with the problem of clustering index entries in the twodimensional domain space that consists of a XML path identifier domain and a filtering data value domain. For performance evaluation, we have compared our proposed 2DG-index with the conventional one dimensional index structure such as the data grouping index (DG-index) and the path grouping index (PG-index). As the result of the performance evaluations, we have verified that our proposed 2DG-index can efficiently support the query processing in XML databases according to the query types.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.29-43
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• 2012

Financial data such as stock index returns and exchange rates have the properties of heavy tail and asymmetry compared to normal distribution. When we estimate VaR using the GARCH model (with the conditional return distribution of normal) it shows the tendency of the lower estimation and clustering in the losses over the estimated VaR. In this paper, we argue that this problem can be resolved through the adaptation of the unbounded Johnson distribution as that of the condition return. We also compare this model with the GARCH with the conditional return distribution of normal and student-t. Using the losses exceed the ex-ante VaR, estimates, we check the validity of the GARCH models through the failure proportion test and the clustering test. We nd that the GARCH model with conditional return distribution of unbounded Johnson provides an appropriate estimation of the VaR and does not occur the clustering of violations.