• Journal of Information Technology Applications and Management
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• v.13 no.1
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• pp.137-147
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• 2006

Spatiotemporal data are records of the spatial changes of moving objects over time. Most data in corporate databases have a spatiotemporal nature, but they are typically treated as merely descriptive semantic data without considering their potential visual (or cartographic) representation. Businesses such as geographical CRM, location-based services, and technologies like GPS and RFID depend on the storage and analysis of spatiotemporal data. Effectively handling the data analysis process may be accomplished through spatiotemporal data warehouse and spatial OLAP. This paper proposes a multidimensional model for spatiotemporal data analysis, and cartographically represents the results of the analysis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.6A
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• pp.870-884
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• 2000

Among OLAP(On-Line Analytical Processing) approaches, ROLAP(Relational OLAP) based on the star, snowflake schema which offer the multidimensional analytical method has performance problem and MOLAP (Multidimensional OLAP) based on Multidimensional Database System has scalability problem. In this paper, to solve the limitaions of previous approaches, design and implementation of multidimensional data model based on Object-Relation DBMS was proposed. With the extensibility of Object-Relation DBMS, it is possible to advent multidimensional data model which more expressively define multidimensional concept and analysis functions that are optimized for the defined multidimensional data model. In addition, through the hierarchy between data objects supported by Object-Relation DBMS, the aggregated data model which is inherited from the super-table, multidimensional data model, was designed. One these data models and functions are defined, they behave just like a built-in function, w th the full performance characteristics of Object-Relation DBMS engine.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.163-165
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• 2003

Some multivariate analyses provide configurations for variables or objects in low dimensional space because we can see easily their relation. In particular, in simple correspondence analysis(SCA), we can obtain the various configurations which are called SCA Maps based on the algebraic algorithms. Moreover, it often occur the variability among them. Therefore, in this study, we will give a comparison of variability of SCA maps using the procrustes analysis which is a technique of comparing configurations in multidimensional scaling.

• Korean Management Science Review
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• v.34 no.2
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• pp.57-66
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• 2017

The purpose of this study is to analyze the multidimensional productivity of the shipbuilding performance and to explain the role of different factors, such as man-hour, dock period, number of building block, launching process rate, automatic welding percent, and drawing fault rate which are important production-related variables in most shipbuilding companies. The shipbuilding productivity is obtained using Data Envelopment Analysis (DEA) approach. Then, a Tobit model is considered to measure the influence of different factors on the measured productivity. The results reveal that this productivity measure can substitute a representative shipbuilding productivity index (CGT/man-hour) in shipbuilding industries. Also, this multidimensional productivity analysis using DEA and Tobit reveals complex relationships between production-related variables and CGT and sale.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.297-306
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• 2004

It is usual that multivariate data analysis produces related (small number of) artificial variates for data reduction. Among them, refer to MDS(multidimensional scaling), MDPREF(multidimensional preference analysis), CDA(canonical discriminant analysis), CCA(canonical correlation analysis) and FA(factor analysis). Varimax rotation of artificial variables which is originally invented in FA for easy interpretations is applied to diverse multivariate techniques mentioned above. Real data analysisis is performed in order to manifest that rotation improves interpretations of artificial variables.

• Journal of Korean Elementary Science Education
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• v.39 no.1
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• pp.145-153
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• 2020

Students' engagement is very important for effect science learning. Multidimensional approaches on students' engagement defines engagement in three ways which includes cognitive, behavioral, and cognitive engagement. Based on the multidimensional approaches on students' engagement, this study identified latent groups of elementary students characterized by patterns of cognitive, behavioral, and emotional engagement in science classes. This study also compared students' perceptions of their engagement in general science classes and small-group activities by the latent groups. 377 elementary students were involved in this study. 5-scale Likert survey were used in order to investigate students' engagement in science classes. Latent class analysis using Mplus program identified 3 latent groups of students engagement in science classes: Highly engaged, moderately engaged, and minimally engaged in three ways of engagement. The mean scores of cognitive, behavioral, and emotional engagement were significantly different by three latent groups. In addition, there were significant difference in students perceptions on participating experiments activities and carefully listening of teacher among latent groups. However, there was no significant difference in students' perceptions on their actions during small-group activities. Educational implications were discussed.

• Korean Journal of Social Welfare
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• v.63 no.1
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• pp.85-111
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• 2011

This study has the purpose to measure the multidimensional poverty in Korea by the counting approach which was theorized by Alkire and Foster to overcome problems of unidimensional approach, union method and intersection method for the identification of the multidimensional poor. By the counting approach applying to Welfare Panel in Korea during 2006-2008, the head-count ratio of the multidimensional poverty was measured. When 3 dimensions are applied as a dimension poverty line, the multidimensional poverty rate was 20% in 2008. It was due to broad deprivations in assets, social securities, income and health. Vulnerable classes such as single parent families, low-education level group, the aged, economically non-active population were among the severe poverty rates, which were reaching around 50%. The analysis reveals the possible alternative to change the present public assistance program to the robust approach of multidimensional poverty measurement, the counting approach. Social policies to reduce poverty in Korea would gain expected positive outcome with the various approaches based on the concepts of multidimensional poverty.

• The Journal of Bigdata
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• v.1 no.1
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• pp.53-60
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• 2016

Data of central government and local government was collected automatically from the public data portal. And we did the multidimensional analysis based on various perspective like file format and present condition of public data. To complete this work, we constructed Data Warehouse based on the other countries' evaluation index case. Finally, the result from service level evaluation by using multidimensional analysis was used to display each area, establishment, fields.

• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.567-578
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• 2017

Grouped multivariate data can be tested for differences between two or more groups using multivariate analysis of variance (MANOVA). However, this method cannot be used if several assumptions of MANOVA are violated. In this case, multidimensional scaling (MDS) and analysis of distance (AOD) can be applied to grouped dissimilarities based on the various distances. A permutation test is a non-parametric method that can also be used to test differences between groups. MDS is used to calculate the coordinates of observations from dissimilarities and AOD is useful for finding group structure using the coordinates. In particular, AOD is mathematically associated with MANOVA if using the Euclidean distance when computing dissimilarities. In this paper, we study the between and within group structure by applying MDS and AOD to the grouped dissimilarities. In addition, we propose a new test statistic using the group structure for the permutation test. Finally, we investigate the relationship between AOD and MANOVA from dissimilarities based on the Euclidean distance.

• Journal of Computing Science and Engineering
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• v.1 no.2
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• pp.125-160
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• 2007

Data Warehousing and OLAP (On-Line Analytical Processing) have turned into the key technology for comprehensive data analysis. Originally developed for the needs of decision support in business, data warehouses have proven to be an adequate solution for a variety of non-business applications and domains, such as government, research, and medicine. Analytical power of the OLAP technology comes from its underlying multidimensional data model, which allows users to see data from different perspectives. However, this model displays a number of deficiencies when applied to non-conventional scenarios and analysis tasks. This paper presents an attempt to systematically summarize various extensions of the original multidimensional data model that have been proposed by researchers and practitioners in the recent years. Presented concepts are arranged into a formal classification consisting of fact types, factual and fact-dimensional relationships, and dimension types, supplied with explanatory examples from real-world usage scenarios. Both the static elements of the model, such as types of fact and dimension hierarchy schemes, and dynamic features, such as support for advanced operators and derived elements. We also propose a semantically rich graphical notation called X-DFM that extends the popular Dimensional Fact Model by refining and modifying the set of constructs as to make it coherent with the formal model. An evaluation of our framework against a set of common modeling requirements summarizes the contribution.