• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.130-134
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• 2010

When an XML document is updated in order to represent correctly the structural relationships of nodes in a document, the existing XML labeling schemes relabel nodes or use a labeling scheme that the label of a node has much information. However, the relabeling on large XML documents needs many labeling costs and the labeling scheme that the label of a node has much information requires many storage costs. Therefore, the existing labeling schemes degrade significantly query processing performance on dynamic XML documents. This paper proposes the bit-pattern labeling scheme that solves the problems of the existing schemes. The proposed labeling scheme outperforms the existing labeling schemes because the structural relationships of nodes are represented with a bit string.

• Journal of KIISE:Databases
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• v.33 no.1
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• pp.56-68
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• 2006

For tracing tag locations, the trajectories should be modeled and indexed in radio frequency identification (RFID) systems. The trajectory of a tag can be represented as a line that connects two spatiotemporal locations captured when the tag enters and leaves the vicinity of a reader. If a tag enters but does not leave a reader, its trajectory is represented only as a point captured at entry. Because the information that a tag stays in a reader is missing from the trajectory represented only as a point, it is impossible to find the tag that remains in a reader. To solve this problem we propose the data model in which trajectories are defined as time-parameterized intervals and new index scheme called the Time Parameterized Interval R-tree. We also propose new insert and split algorithms that reduce the area of nodes to enable efficient query processing. We evaluate the performance of the proposed index scheme and compare it with previous indexes on various datasets.

• Journal of KIISE:Databases
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• v.33 no.1
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• pp.1-11
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• 2006

It is possible to track the moving vehicle as well as to develop the location based services actively according to the progress of wireless telecommunication and GPS, to the spread of network, and to the miniaturization of cellular phone. To provide these location based services, it is necessary for an index technique to store and search too much moving object data rapidly. However the existing indices require a lot of costs to insert the data because they store every position data into the index directly. To solve this problem in this paper, we propose a buffer node operation and design a GU-tree(Group Update tree). The proposed buffer node method reduces the input cost effectively since the operation stores the moving object location data in a group, the buffer node as the unit of a non-leaf node. hnd then we confirm the effect of the buffer node operation which reduces the insert cost and increase the search performance in a time slice query from the experiment to compare the operation with some existing indices. The proposed tufter node operation would be useful in the environment to update locations frequently such as a transportation vehicle management and a tour-guide system.

• Journal of Information Technology Services
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• v.8 no.3
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• pp.171-184
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• 2009

Since the development of the Distributed Hash Tables (DHTs), the distributed lookup services are one of the hot topics in the networking area. The main reason of this popularity is the simplicity of the lookup structure. However, the simple key based search mechanism makes the so called "keyword" based search difficult if not impossible. Thus, the applicability of the DHTs is limited to certain areas. In this paper. we find that DHTs can be used as the ubiquitous sensor network (USN) metadata lookup service across a large number of sensor networks. The popularity of the Ubiquitous Sensor Network has motivated the development of the USN middleware services for the sensor networks. One of the key functionalities of the USN middleware service is the lookup of the USN metadata, by which users get various information about the sensor network such as the type of the sensor networks and/or nodes, the residual of the batteries, the type of the sensor nodes. Traditional distributed hash table based lookup systems are good for one sensor network. However, as the number of sensor network increases, the need to integrate the lookup services of many autonomous sensor networks so that they can provide the users an integrated view of the entire sensor network. In this paper, we provide a hybrid lookup model, in which the autonomous lookup services are combined together and provide seamless services across the boundary of a single lookup services. We show that the hybrid model can provide far better lookup performance than a single lookup system.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1372-1381
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• 2017

In this paper, a texture feature extraction method using local energy and local correlation of Gabor transformed images is proposed and applied to an image retrieval system. The Gabor wavelet is known to be similar to the response of the human visual system. The outputs of the Gabor transformation are robust to variants of object size and illumination. Due to such advantages, it has been actively studied in various fields such as image retrieval, classification, analysis, etc. In this paper, in order to fully exploit the superior aspects of Gabor wavelet, local energy and local correlation features are extracted from Gabor transformed images and then applied to an image retrieval system. Some experiments are conducted to compare the performance of the proposed method with those of the conventional Gabor method and the popular rotation-invariant uniform local binary pattern (RULBP) method in terms of precision vs recall. The Mahalanobis distance is used to measure the similarity between a query image and a database (DB) image. Experimental results for Corel DB and VisTex DB show that the proposed method is superior to the conventional Gabor method. The proposed method also yields precision and recall 6.58% and 3.66% higher on average in Corel DB, respectively, and 4.87% and 3.37% higher on average in VisTex DB, respectively, than the popular RULBP method.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.1-7
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• 2006

Recently, studies of relevance feedback to increase the performance of image retrieval has been activated. In this Paper a new region weighting method in region based image retrieval with relevance feedback is proposed to reduce the semantic gap between the low level feature representation and the high level concept in a given query image. The new weighting method determines the importance of regions according to the spatial locations of regions in an image. Experimental results demonstrate that the retrieval quality of our method is about 18% in recall better than that of area percentage approach. and about 11% in recall better than that of region frequency weighted by inverse image frequency approach and the retrieval time of our method is a tenth of that of region frequency approach.

• Journal of the Korea Society of Computer and Information
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• v.20 no.1
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• pp.85-92
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• 2015

As computer game maps get more elaborate, path-finding by using $A^*$ algorithm in grid-based game maps becomes bottlenecks of the overall game performance. It is because the search space becomes large as the number of nodes increases with detailed representation in cells. In this paper we propose an efficient pathfinding method in which the computer game maps in a regular grid is converted into the polygon-based representation of the list of vertices and then the visibility information about vertices of polygons can be utilized. The conversion to the polygon-based map does not give any effect to the real-time query process because it is preprocessed offline. The number of visited nodes during search can be reduced dramatically by designing heuristics using visibility information of vertices that make the accuracy of the estimation enhanced. Through simulations, we show that the proposed methods reduce the search space and the search time effectively while maintaining the advantages of the grid-based method.

• Geomechanics and Engineering
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• v.18 no.4
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• pp.339-352
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• 2019

Rockmass parameters are used in the design of engineering structures built in rock and soil. One of the most important of these parameters is the rockmass Emass (Emass). Determination of the Emass of rockmass is a long, hard and expensive job. Therefore, empirical formulas developed by different researchers are used. These formulas use the elastic modulus of the material as a parameter. This value is a constant value in the design. However, engineering structures remain under different loads depending on many factors, such as topography, geometry of the structure, rock / soil properties. Time is other important parameter for rock/soil structure. With the start of the excavation, the loads that the structure is exposed to will change and remain constant at one level. In the new proposed method, the use of different Emass calculated from empirical formulas using the different material elastic modulus, which has different values under different loads as time dependent, was investigated in rock/soil structures during design. The performance of the stability analysis using different deformation modules was questioned by numerical modeling method. For this query, a sub-routine which can be integrated into the numerical modeling software has been developed. The integrated sub-routine contains the formula for the Emass, which is calculated from the material elasticity modules under time dependent and different constant loads in the laboratory. As a result of investigations conducted in 12 different field studies, the new proposed method is very sensitive.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.6
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• pp.15-23
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• 2020

While research activities in the agricultural field for climate change are being actively carried out, smart agriculture using information and communication technology has become a new trend in line with the Fourth Industrial Revolution. Accordingly, research is being conducted to identify and respond to signs of abnormal growth in advance by monitoring the stress of crops in various outdoor environments and soil conditions. There are also attempts to analyze data collected in real time through various sensors using artificial intelligence techniques or big data technologies. In this paper, we propose a big data model that is effective in analyzing the growth environment informations and biometric information of crops by using the existing relational database for big data analysis. The performance of the model was measured by the response time to a query according to the amount of data. As a result, it was confirmed that there is a maximum time reduction effect of 23.8%.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.406-412
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• 2022

Storing large amounts of data has always been a big problem from the beginning of computing history. Big Data has made huge advancements in improving business processes by finding the customers' needs using prediction models based on web and social media search. The main purpose of big data stream processing frameworks is to allow programmers to directly query the continuous stream without dealing with the lower-level mechanisms. In other words, programmers write the code to process streams using these runtime libraries (also called Stream Processing Engines). This is achieved by taking large volumes of data and analyzing them using Big Data frameworks. Streaming platforms are an emerging technology that deals with continuous streams of data. There are several streaming platforms of Big Data freely available on the Internet. However, selecting the most appropriate one is not easy for programmers. In this paper, we present a detailed description of two of the state-of-the-art and most popular streaming frameworks: Apache Ignite and Hazelcast. In addition, the performance of these frameworks is compared using selected attributes. Different types of databases are used in common to store the data. To process the data in real-time continuously, data streaming technologies are developed. With the development of today's large-scale distributed applications handling tons of data, these databases are not viable. Consequently, Big Data is introduced to store, process, and analyze data at a fast speed and also to deal with big users and data growth day by day.