• Journal of Information Science Theory and Practice
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• v.10 no.4
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• pp.38-52
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• 2022

Since older people are traditionally considered disadvantaged when it comes to Internet use, it is useful to examine whether older individuals use the Internet for health information seeking (HIS). This study aims to investigate digital inequalities in terms of Internet use by older population for HIS in the European region. As methods, we applied secondary data analysis (of Eurostat data) to investigate the influence of age, educational level, sex, and countries' wealth. Cluster analysis combined with multidimensional scaling was used to find out those countries exhibiting similarities in older people's online HIS. The main results are: Older individuals do not equally use the Internet in general and for HIS in particular. Older Internet users with higher level of education and of the female sex are more likely to use the Internet for health information.

• International Journal of Contents
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• v.16 no.4
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• pp.39-49
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• 2020

The goal of this research was to understand the patterns of multidimensional engagement in MOOCs. An email with an online survey link was sent to enrollees in an MOOC course. The survey included 35 questions asking about engagement, teaching presence, and learning persistence. The items were validated in the literature, revised for the MOOC setting, reviewed by four professionals in the field of educational technology, and used in the study. A heterogeneous group of 170 individuals gathered through convenience sampling participated in the study. With cluster analysis of the engagement data, three groups were identified: Cluster1, 2, and 3. Cluster 1 scored high on behavioral, emotional, and cognitive engagement. Cluster 2 scored high on behavioral aspects but low on emotional and cognitive engagement. Cluster 3 scored low on behavioral and cognitive engagement but high on emotional aspects. The study addressed cluster-specific learner characteristics and differences in perceived teaching presence and learning persistence. Design strategies pertaining to each cluster were further discussed. These strategies may guide instructors and practitioners in the design and management of MOOCs and should be further validated through future studies.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.133-134
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• 2020

본 논문에서는 낙후된 운전면허 학원 시스템 개선을 위한 운전면허 수험생 맞춤관리 어플리케이션을 제안한다. 제안하는 어플리케이션은 수강생 맞춤 가이드, 후기 서비스, 스케줄 조정, 채점표 제공 등의 다양하고 체계적인 서비스를 도입하여 기존의 시스템을 효과적으로 개선한다. 또한 기존에 개발되어 있는 운전면허 관련 어플리케이션과 달리 오프라인에서만 가능했던 부분까지 온라인 맞춤 관리로 포함하여 개발함으로서 사용자의 만족도를 향상시키도록 한다.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.24 no.4
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• pp.99-113
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• 2013

The study investigated the intellectual structure of the library and information science field by journal co-citation analysis on thirty LIS journals with high journal impact factors. Patterns of journal-to-journal citation show the structure of journals in the field, visualized by a networked map of the journals. The result shows journals that can be considered as the core group and as the peripheral group, which corresponds to other studies for investigating the field's intellectual structure using other techniques, such as cluster analysis and multidimensional scaling.

• Journal of Korean Library and Information Science Society
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• v.32 no.3
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• pp.169-197
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• 2001

This study investigated the intellectual structure of Korean library and information science and its change in the 1990s using author co-citation analysis. The citation data came from in 3 journals in the field of library and information science from 1990 through 1999, and 50 authors were selected and analyzed in detail by means of multi-variate statistical techniques such as multidimensional scaling, cluster analysis, factor analysis and crosstab analysis in order to excess the intellectual structure of discipline and its changing research patterns.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.196-204
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• 2012

Recently, many applications perform OLAP(On-Line Analytical Processing) over a very large volume of data. Multidimensional data cube is regarded as a core tool in OLAP analysis. This paper focuses on the method how to efficiently compute data cubes in parallel by using a popular parallel processing tool, MapReduce. We investigate efficient ways to implement PipeSort algorithm, a well-known data cube computation method, on the MapReduce framework. The PipeSort executes several (descendant) cuboids at the same time as a pipeline by scanning one (ancestor) cuboid once, which have the same sorting order. This paper proposed four ways implementing the pipeline of the PipeSort on the MapReduce framework which runs across 20 servers. Our experiments show that PipeMap-NoReduce algorithm outperforms the rest algorithms for high-dimensional data. On the contrary, Post-Pipe stands out above the others for low-dimensional data.

• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.67-75
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• 2018

Multidimensional scaling (MDS) is an exploratory analysis of multivariate data to represent the dissimilarity among objects in the geometric low-dimensional space. However, a general MDS map only shows the information of objects without any information about variables. In this study, we used MDS based on the algorithm of Torgerson (Theory and Methods of Scaling, Wiley, 1958) to visualize some clusters of objects in categorical data. For this, we convert given data into a multiple indicator matrix. Additionally, we added the information of levels for each categorical variable on the MDS map by applying the partition method of Shin et al. (Korean Journal of Applied Statistics, 28, 1171-1180, 2015). Therefore, we can find information on the similarity among objects as well as find associations among categorical variables using the proposed MDS map.

• Korean Journal of Adult Nursing
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• v.23 no.4
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• pp.363-373
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• 2011

Purpose: Fatigue is a common problem in Parkinson's disease (PD), affecting 30~65% of patients with that diagnosis. Only recently has fatigue been recognized as an important clinical feature of PD. The aim of this study was to investigate the level of fatigue and related factors in patients with PD. Methods: Between March 1, and September, 30, 2010, a sample of 181 PD patients agreed to be interviewed. Results: The female patients' PFS (Parkinson Fatigue Scale) score was higher than those of the male patients. Household income and having a Job were significantly correlated with the PFS scores. Among the disease characteristics, motor fluctuations, dyskinesia and modified Hoehn and Yahr stage were significantly correlated with the PFS scores. On stepwise regression analysis, the most important factors related to the PFS scores were depression and sleep disturbance. Conclusion: Fatigue in patients with PD was associated with many factors and strongly associated with depression and sleep disturbance. Fatigue is a multidimensional construct; therefore, multidimensional strategies for relieving specific aspects of fatigue are needed.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.1
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• pp.571-578
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• 2013

Background: Patients with breast cancer can experience a feeling of hopelessness very deeply in the adjustment process, and the social support provided during this period can be effective in increasing the level of hope. The present study aimed to identify breast cancer patients' social support and hopelessness level. Materials and Methods: The target population of this analytical study was all breast cancer patients (total of 85) who had treatment in the oncology department of a university hospital located in Adana/Turkey and who met the inclusion criteria. Data were collected through "Personal Information Form", "Beck Hopelessness Scale (BHS)" and "Multidimensional Scale of Perceived Social Support" (MSPSS). Analysis was performed using Shapiro Wilk, One Way ANOVA Welch, Student t-test, Mann Whitney U, and Kruskall Wallis tests. Homogeneity of variance was tested with the Levene, Bonferroni and Games Howell tests. Mean scores and standard deviation values are given as descriptive statistics. Results: Average age of the participants with breast cancer is $48.6{\pm}10.6$. Of all the participants, 84.7% are married, 49.4% graduated from primary school, 81.2% are housewives, and 82.4% had children. The participants' multidimensional perceived social support total scores were found to be high ($57.41{\pm}13.97$) and hopelessness scale scores low ($5.49{\pm}3.80$). There was a reverse, linear relationship between hopelessness scale scores and social support total scores (r=-0.259, p=0.017). A statistically significant relationship was found between hopelessness scores and education level and having children, occupation, income status, and education level of spouses (p<0.05). Conclusions: The present study indicates that hopelessness of the patients with breast cancer decreased with the increase in their social support. Therefore, activating patient social support systems is of importance in increasing their level of hope.

• The Journal of the Korea Contents Association
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• v.10 no.2
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• pp.99-110
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• 2010

An event means a flow which has a time attribute such as a symptom of patient. Stream data collected by sensors can be summarized as an interval event which has a time interval between the start-time point and the end-time point in multiple stream data environment. Most of temporal mining techniques have considered only the frequent events. However, these approaches may ignore the infrequent event even if it is important. In this paper, we propose a new temporal data mining that can find association rules for the significant temporal relation based on interval events in multidimensional stream data environment. Our method considers the weight of events and stream data on the sensing time point of abnormal events. And we can discover association rules on the significant temporal relation regardless of the occurrence frequency of events. The experimental analysis has shown that our method provide more useful knowledge than other conventional methods.