• Journal of Soil and Groundwater Environment
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• v.15 no.6
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• pp.122-127
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• 2010

Constructing the national inventory that can be used as a tool to identify and assess existing or potential contamination is necessary for efficiently managing the soil and groundwater contamination. In order to start this construction, the first step is how we define and classify potential contamination sources of soil and groundwater. After selecting the basic classification model of contamination sources from developed countries, we suggested the classification and list of the potential contamination sources of soil and groundwater which are appropriate for specific conditions of South Korea. In addition, we investigated several databases to confirm the existence of available data sources and then examined established attribute data through chemical accident response information system (CARIS) and water information system (WIS) in National Institute of Environmental Research and mine geographic information system (MGIS) in Mine Reclamation Corporation. All sorts of attribute data in the existing databases can be utilized as significant assessment factors for determining the management priority of potential contamination sources in the future. Therefore, it is required the expanded investigation of additional database sources and the continual modification so that the classification system of potential contamination sources can be improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3108-3120
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• 2019

Point cloud is widely used in 3D applications due to the recent advancement of 3D data acquisition technology. Polygonal mesh-based compression has been dominant since it can replace many points sharing a surface with a set of vertices with mesh structure. Recent point cloud-based applications demand more point-based interactivity, which makes point cloud compression (PCC) becomes more attractive than 3D mesh compression. Interestingly, an exploration activity has been started to explore the feasibility of PCC standard in MPEG. In this paper, a new color attribute compression method is presented for point cloud data. The proposed method utilizes the spatial redundancy among color attribute data to construct a color palette. The color palette is constructed by using K-means clustering method and each color data in point cloud is represented by the index of its similar color in palette. To further improve the compression efficiency, the spatial redundancy between the indices of neighboring colors is also removed by marking them using a flag bit. Experimental results show that the proposed method achieves a better improvement of RD performance compared with that of the MPEG PCC reference software.

• Journal of the Korea Society of Computer and Information
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• v.20 no.9
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• pp.11-20
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• 2015

In order to prevent the elderly people from committing suicide attempts, it is necessary to verify attributes that affect the suicide attempts. It is noted that previous studies have focused on qualitative approaches, and simple correlation analyses to determine the attributes related to the suicide attempts in the elderly people. However, such previous approaches had led to insufficient performance when facing with complicated data sets. In this sense, this study suggests an alternative method in which attribute selection techniques are adopted to determine more relevant attributes of the suicide attempts occurring in Korean elderly people. To verify empirical validity of our proposed method, we used Korea National Health and Nutrition Examination Survey (KNHANES) from January 2007 to December 2012. Empirical results proved that the proposed attribute selection techniques showed better predictive effectiveness; 94.4% compared to the simple statistical methods. This study proposes a way to determining the elderly suicide and preventing it to happen.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.3
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• pp.118-128
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• 2012

An attempt is given to the problem of analyzing the two-way binary attribute data using the logistic regression model in order to find a sound statistical methodology. It is demonstrated that the analysis of variance (ANOVA) may not be good enough, especially for the case that the proportion is very low or high. The logistic transformation of proportion data could be a help, but not sound in the statistical sense. Meanwhile, the adoption of generalized least squares (GLS) method entails much to estimate the variance-covariance matrix. On the other hand, the logistic regression methodology provides sound statistical means in estimating related confidence intervals and testing the significance of model parameters. Based on simulated data, the efficiencies of estimates are ensured with a view to demonstrate the usefulness of the methodology.

• Journal of KIISE:Databases
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• v.35 no.4
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• pp.319-332
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• 2008

Data cube, which is multi-dimensional data model, have been successfully applied in many cases of multi-dimensional data analysis, and is still being researched to be applied in data stream analysis. Data stream is being generated in real-time, incessant, immense, and volatile manner. The distribution characteristics of data arc changing rapidly due to those characteristics, so the primary rule of handling data stream is to check once and dispose it. For those characteristics, users are more interested in high support attribute values observed rather than the entire attribute values over data streams. This paper propose dynamic data cube for applying data cube to data stream environment. Dynamic data cube specify user's interested area by the support ratio of attribute value, and dynamically manage the attribute values by grouping each other. By doing this it reduce the memory usage and process time. And it can efficiently shows or emphasize user's interested area by increasing the granularity for attributes that have higher support. We perform experiments to verify how efficiently dynamic data cube works in limited memory usage.

• Journal of Digital Convergence
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• v.13 no.1
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• pp.263-268
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• 2015

Put your information in the object-based sensors and mobile networks has been developed that correlate with ubiquitous information technology as the development of IT technology. However, a security solution is to have the data stored in the server, what minimal conditions. In this paper, we propose a data management method is applied to a hash chain of the properties of the multiple techniques to the data used by the big user and the data services to ensure safe handling large amounts of data being provided in the big data services. Improves the safety of the data tied to the hash chain for the classification to classify the attributes of the data attribute information according to the type of data used for the big data services, functions and characteristics of the proposed method. Also, the distributed processing of big data by utilizing the access control information of the hash chain to connect the data attribute information to a geographically dispersed data easily accessible techniques are proposed.

• The Journal of Information Technology and Database
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• v.6 no.2
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• pp.71-86
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• 1999

The performance of relational database is measured by the number of disk accesses necessary to transfer data from disk to main memory. The paper proposes to vertically partition relations into fragments and to allow attribute replication to reduce the number of disk accesses. To reduce the computational time, heuristic search method using genetic algorithm is used. Genetic algorithm used employs a rank-based-sharing fitness function and elitism. Desirable parameters of genetic algorithm are obtained through experiments and used to find the solutions. Solutions of attribute replication and attribute non-replication problems are compared. Optimal solutions obtained by branch and bound method and by heuristic solutions(genetic algorithm) are also discussed. The solution method proposed is able to solve large-sized problems within acceptable time limit and shows solutions near the optimal value.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.122-127
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• 1998

This paper suggests an integrated method for discovering knowledge from a large database. Our approach applies an attribute-oriented concept hierarchy ascension technique to extract generalized data from actural data in databases, induction of decision trees to measure the value of information, and knowledge reduction of rough set theory to remove dispensable attributes and attribute values. The integrated algorithm first reduce the size of database for the concept generalization, reduces the number of attributes by way of elimination condition attributes which have little influence on decision attribute, and finally induces simplified decision rules removing the dispensable attribute values by analyzing the dependency relationships among the attributes.

• Journal of the Korean Society of Food Culture
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• v.22 no.6
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• pp.794-800
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• 2007

The purpose of this study was to measure the tourists' preference for alternative restaurants with different combinations of 4 attribute levels: origin description, food type, price and service guarantee. A total of 210 questionnaires were completed from tourists who visited Kwangyang, Suncheon and Yeosu during Jan. 2 - Jan. 15, 2007. Conjoint experiment method was used to develop hypothetical restaurants. Ordinal probit model was used to measure the effects of attribute levels on the tourists' preference. Results of the study demonstrated that the ordinal probit model analysis result for the data indicated excellent model fit. The effects of attribute levels (origin description, traditional food, fusion food, price, service guarantee) on the tourists' preference were statistically significant. As expected, estimates of marginal willingness to pay for origin description(3.063), food type(2.349), and service guarantee(2.356) were statistically significant. Moreover, tourists were more willing to pay for origin description than other attribute levels. Tourists also considered the origin description as the very important attribute. In conclusion, based on conjoint analysis, a model was proposed of marginal willingness to pay of attribute levels. It should be noted that the original model was modified and should, preferably, be validated in future research.

• Fashion & Textile Research Journal
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• v.8 no.6
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• pp.647-654
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• 2006

The purpose of this study is to identify the differences among goods attribute and brand awareness on fashion brand type. we were intended to suggest characteristics of each consumer group by identifying the differences of consumers' purchasing activities. 672 of consumers by brand who frequently purchase casual brand were chosen for the analysis according to common brand classification of national brand, private brand and no brand. For the purpose of data analysis, we performed factorial analysis of measuring tools and credibility test. Concerning the differences of goods attribute, brand awareness by brand type, MANOVA, ANOVA was employed, complimented with Sheffe-test as a post hoc test in case of occurrence of any differences by group. The findings from the analysis are described in the following. Regarding goods attribute by fashion brand type, there existed a significant difference between brand types in all the sub factors of goods attribute such as product attribute, shop attribute, and price attribute. Especially, the difference of product attribute is much more significant in the areas of material suitableness, product assortment, aesthetic expression, size & quality, clothing maintenance, and clothing comfortableness. In case of shop attribute, there was a significant difference between groups in all the factors such as shop environment, convenience of shopping, sales promotion, service quality of sales clerk, location, and shop reputation. Concerning price attribute, we found a significant difference between groups in the factors of price value, price reasonableness, price information, and economical efficiency of price. As for the difference of brand awareness by brand type, among other factors, brand value had a difference between groups; that is, private brand was found to obtain the highest brand value awareness.