• Journal of Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.89-96
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• 2016

By reviewing preceding studies of big data and spatial big data, spatial big data was defined as one part of big data, which spatialize location information and systematize time series data. Spatial big data, as one part of big data, should not be separated with big data and application methods within the system is to be examined. Therefore in this study, services that spatial big data is required to provide were suggested. Spatial big data must be available of various spatial analysis and is in need of services that considers present and future spatial information. Not only should spatial big data be able to detect time series changes in location, but also analyze various type of big data using attribute information of spatial data. To successfully provide the requirements of spatial big data and link various type of big data with spatial big data, methods of forming sample points and extracting attribute information were proposed in this study. The increasing application of spatial information related to big data is expected to attribute to the development of spatial data industry and technological advancement.

• Culinary science and hospitality research
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• v.23 no.4
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• pp.66-73
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• 2017

This study was performed to verify the relationships among selection attribute, restaurant trust, experiential value, and recommendation focusing on sport center consumers. The data were collected from visitors who registered more than three months in the sport center in South Korea. Total 500 survey was distributed and 330 participants were used for further statistical analysis. SPSS 23.0 and AMOS 21.0 for Windows were used for statistical analysis. Five factors of selection attribute (menu, interior, exterior, staff, convenience) were extracted, and measured by using 15 questions. According to the results of this study, interior, exterior, and staff factors have positive effects on restaurant trust, and interior and menu were significant predictors of the experiential value. In addition, present study confirmed the theoretical relationship among trust, experiential value, and recommend intention as perceived by sport center visitors. Although there are many studies which demonstrated the relationships among selection attribute and other outcome variables, little research explained the relationships among the variables from sport center consumers. Therefore, this study will contribute to provide meaningful results and some practical implications for both academia and the related foodservice industry.

• The Research Journal of the Costume Culture
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• v.10 no.6
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• pp.617-632
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• 2002

The purposes of this study are 1) to classify the consumer group according to clothing purchase store(department store, road shop, discount store, Dongdaemoon & Namdaemoon markets) 2) to analyze the differences between VMD attributes which each consumer group value. The attributes on the VMD are categorized as 5 areas 1) interior 2) show window 3) product display & display change cycle 4) color · light · music · small instrument 5) promotion(POP & salesperson). The data were collected from 238 females students and were analyzed by frequency, percent and X²-test. The results of this study are as fellows . 1) On the attribute of Interior, there were significant differences in terms of flow in a store, rest area, the cleanness of floor, show case, and the merchandise itself. 2) On the attribute of show window, there were significant differences in terms of interst of show window. 3) On the attribute of product display & display change cycle, there were significant differences in terms of display method, and display change cycle. 4) On the attribute of color · light · music · small instrument, there were significant differences in terms of interest of color coordination, luminosity and effect of light, and necessity of music & small instrument. 5) On the attribute of promotion(POP & salesperson), there were significant differences in terms of aid of POP.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.46
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• pp.33-49
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• 1998

In order to improve the performance of relational databases, one has to reduce the number of disk accesses necessary to transfer data from disk to main memory. The paper proposes to reduce the number of disk I/O accesses by vertically partitioning relation into fragments and allowing attribute replication to fragments if necessary. When zero-one integer programming model is solved by the branch-and-bound method, it requires much computing time to solve a large sized problem. Therefore, heuristic solutions using genetic algorithm(GA) are presented. GA in this paper adapts a few ideas which are different from traditional genetic algorithms, for examples, a rank-based sharing fitness function, elitism and so on. In order to improve performance of GA, a set of optimal parameter levels is determined by the experiment and makes use of it. As relations are vertically partitioned allowing attribute replications and saved in disk, an attribute replicating vertical partition method by GA can attain less access cost than non-attribute-replication one and require less computing time than the branch-and-bound method in large-sized problems. Also, it can acquire a good solution similar to the optimum solution in small-sized problem.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.76-78
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• 2014

Naive Bayes (NB, for shortly) learning is more popular, faster and effective supervised learning method to handle the labeled datasets especially in which have some noises, NB learning also has well performance. However, the conditional independent assumption of NB learning imposes some restriction on the property of handling data of real world. Some researchers proposed lots of methods to relax NB assumption, those methods also include attribute weighting, kernel density estimating. In this paper, we propose a novel approach called NB Based on Attribute Weighting in Kernel Density Estimation (NBAWKDE) to improve the NB learning classification ability via combining kernel density estimation and attribute weighting.

• Journal of KIISE:Databases
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• v.37 no.1
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• pp.1-11
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• 2010

Recently, there has been a growing interest in the processing of continuous queries over multiple data streams. When the arrival rates of tuples exceed the memory capacity of the system, a load shedding technique is used to avoid the system becoming overloaded by dropping some subset of input tuples. In this paper, we propose an effective load shedding algorithm for multi-way windowed joins over multiple data streams. Most previous load shedding algorithms estimate the productivity of each tuple, i.e., the number of join output tuples produced by the tuple, based on its "join attribute value" and drop tuples with the lowest productivity. However, the productivity of a tuple cannot be accurately estimated from its join attribute value when the join attribute values are unique and do not repeat, or the distribution of the join attribute values changes over time. For these cases, we estimate the productivity of a tuple based on its "arrival order" on data streams, rather than its join attribute value. The proposed method can effectively estimate the productivity of a tuple even when the productivity of a tuple cannot be accurately estimated from its join attribute value. Through extensive experiments and analysis, we show that our proposed method outperforms the previous methods in terms of effectiveness and efficiency.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2000.11a
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• pp.103-106
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• 2000

Geophysical data sets from the Chojeong area in the Chungbuk-Do are compositely studied in terms of multi-attribute interpretation for the subsurface mapping of shallow fracture zones, associated with groundwater reservoir. Utilizing a GIS software, the attribute data are implemented to a database; a lineament from the satellite image, electrical resistivities and its standard deviation, radioactivity, seismic velocity, bedrock depth from exploration data. In an attempt to interpret 1-D electrical sounding data in 2-D and 3-D views, 2-D resistivities structures are firstly made by interpolating 1-D plots. Reconstruction of a resistivity volume is found to be an effective scheme for subsurface mapping of shallow fracture zones. Shallow fracture zones in the southeastern part of the study area are commonly correlated in the various exploration data.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.1
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• pp.1-7
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• 2005

Data mining is widely used for turning huge amounts of data into useful information and knowledge in the information industry in recent years. When analyzing data set with continuous values in order to gain knowledge utilizing data mining, we often undergo a process called discretization, which divides the attribute's value into intervals. Such intervals from new values for the attribute allow to reduce the size of the data set. In addition, discretization based on rough set theory has the advantage of being easily applied. In this paper, we suggest a discretization algorithm based on Rough Set theory and SOM(Self-Organizing Map) as a means of extracting valuable information from large data set, which can be employed even in the case where there lacks of professional knowledge for the field.

• Journal of the Korean Society of Clothing and Textiles
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• v.45 no.6
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• pp.949-964
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• 2021

Accumulation of high-quality data is crucial for AI learning. The goal of using AI in fashion service is to propose of a creative, personalized solution that is close to the know-how of a human operator. These customized solutions require an understanding of fashion products and emotions. Therefore, it is necessary to accumulate data on the attributes of fashion products and fashion emotion. The first step for accumulating fashion data is to standardize the attribute with coherent system. The purpose of this study is to propose a fashion emotional classification system. For this, images of fashion products were collected, and metadata was obtained by allowing consumers to describe their emotions about fashion images freely. An emotional classification system with a hierarchical structure, was then constructed by performing frequency and CONCOR analyses on metadata. A final classification system was proposed by supplementing attribute values with reference to findings from previous studies and SNS data.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.2
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• pp.11-19
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• 2006

Sewerage is the system that improves the quality of human life and prevents many disasters such as floods. However the investigators in Korea only have been concerned about the sewer system, so the sewage treatment plant stays in the basic level like mapping. For example, only one attribute can be recognized in the linear object. Because of this limitation, it makes difficult to manage the linear attribute regarding to the sewage pipe plan. And it is impossible to control a partial (point type, line type) attribute changes of the linear object. We will therefore present the applicable method for the attribute changes of the linear object like the sewage pipe plans. For this reason, this paper is designed on the basis of Dynamic Segmentation(DS). DS has the advantage of giving the attribute value to the exact place in the linear object. As a result of using DS, the variety environment changes around the sewage pipes are applied to the building sewerage data. This also makes it possible to get a precise estimation for the maximum dirty water amount.