• Journal of Digital Convergence
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• v.19 no.5
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• pp.93-103
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• 2021

The special means of transportation introduced to improve the mobility of the transportation vulnerable met the number of legal standards in 2016, but lack of development in terms of quality, such as the existence of long waiting times. In order to streamline the operation of special means of transportation, long-term standby traffic, which is the top 25% of the wait time, was extracted from the Daegu Metropolitan Government's special transportation history data, and spatial autocorrelation analysis and social network analysis were conducted. As a result of the analysis, the correlation between the average waiting time of special transportation users and the space was high. As a result of the analysis of internal degree centrality, the peak time zone is mainly visited by general hospitals, while the off-peak time zone shows high long-term waiting demand for visits by lawmakers. The analysis of external degree centrality showed that residential-based traffic demand was high in both peak and off-peak hours. The results of this study are considered to contribute to the improvement of the quality of the operation of special transportation means, and the academic implications and limitations of the study are also presented.

• Journal of Navigation and Port Research
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• v.38 no.6
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• pp.607-614
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• 2014

This article aims to explore and discuss research trends in global cruise industry using keyword network analysis. We visualize keyword networks in each of four groups of 1982-1999, 2000-2004, 2005-2009, 2010-2014 based on the top 20 keyword nodes' degree centrality and betweenness centrality which are selected among four centrality measurements, comparing them with frequency order. The article shows that keyword frequency collected from 240 articles published in international journals is subject to Zipf's law and nodes degree distribution also exhibits power law. We try to find out research trends in global cruise industry to change some important keywords diachronically, visualizing several networks focusing on the top two keywords, cruise and tourism, belonging to all the four year groups, with high degree and betweenness centrality values. Interestingly enough, a new node, China, connecting the top most keywords, appears in the most recent period of 2010-2014 when China has emerged as one of the rapid development countries in global cruise industry. Therefore keyword network analysis used in this article will be useful to understand research trends in global cruise industry because of increase and decrease of numbers of network types in different year groups and the visual connection between important nodes in giant components.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.85-92
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• 2020

The purpose of this study is to compare and analyze the differences in employment trends between enterprises and job seekers related to the 4th Industrial Revolution, focusing on the 11 elements of recruitment process. As a method of analysis, a methodology suitable for the convergence research methodology was used by mixing social network analysis and variance analysis, and significant results were derived. First, while large enterprises emphasized organizational culture and job analysis, small enterprises emphasized an interview from the perspective of practitioners. Second, in both manufacturing and service industries, enterprises emphasized interviews and documents, but job seekers emphasized job analysis. Third, the proportion of the recruitment process was found to be greater in the manufacturing industry than in the service industry. Fourth, it was found that enterprises accounted for a larger proportion of the recruitment process than job seekers. This showed an interaction effect between the subject and the industry sector. Therefore, the evaluation of the recruitment process between enterprises and job seekers was found to be very different.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.2 no.4
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• pp.19-41
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• 2007

This research has a goal which is suggesting the way of constructing 'Cluster' which mean scheming the commencement of an enterprise in an early stage. Now it is reorganized into a IT industry structure 'Time-to market growth' is burst as a big issue. in that point, this research analyze the core success factor which is drawing from the existing IT industrial complex, and then it will be used to draw up to the 'Idealistic growth-support Cluster' on the basis of it, we pulled out various issues about the Corporate in the early stage of its growth. Therefore, this research is focused on presenting the ideal network(net) by considering the Network that organizations and business in Cluster or the network including the factors linked organizations and business in Cluster. therefore, this research carried out three big analysis. from the case investigation we pulled out the core growth factor, and then we approached the analysis of net structure for making application to Network Analysis. and then we analyzed that the characteristics of the Network after measuring by on the basis of analyzing core growth factor. and especailly, this research carried out the Core analysis for recognition of Core- support-frame by base Centrality Test on the net which is composed of growth support organizations at each Business. Judging from this, we can help to make full use of resources for the network analysis in Cluster and establish the Network Strategy by Structure comparison between the structure of industry-Cluster and ideal Business-support networks on the basis of the analysis from the Core-success-factor

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.99-114
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• 2011

Collaborative Filtering (CF) suffers from two major problems:sparsity and cold-start recommendation. This paper focuses on the cold-start problem for new customers with no purchase records and the sparsity problem for the customers with very few purchase records. For the purpose, we propose a method for the new customer recommendation by using a combined measure based on three well-used centrality measures to identify the customers who are most likely to become neighbors of the new customer. To alleviate the sparsity problem, we also propose a hybrid approach that applies our method to customers with very few purchase records and CF to the other customers with sufficient purchases. To evaluate the effectiveness of our method, we have conducted several experiments using a data set from a department store in Korea. The experiment results show that the combination of two measures makes better recommendations than not only a single measure but also the best-seller-based method and that the performance is improved when applying the hybrid approach.

• The Journal of Society for e-Business Studies
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• v.20 no.1
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• pp.23-43
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• 2015

Information and communications technology facilitates collaboration among individuals by functioning as an open platform for open collaboration projects. In this regard, this study aims to understand the network characteristics of participants who contribute greatly to open collaboration by investigating the mutual cooperation network in an open source project, which represents a form of open collaboration based on social network theory. To achieve this objective, this study analyzes the network centrality of developers with a high number of commits, particularly 8,101 developers in 782 repositories in GitHub, a representative open source platform. This study also determines how the relationship between network centrality and number of commits depends on the size of a repository network and the presence of a hub. Consequently, the number of commits by developers with high degree, betweenness, and closeness centrality is increasing. Among which, betweenness centrality has the highest explanatory power. Furthermore, when a hub is present and as network size increases, the relationship between the betweenness centrality of a developer and his/her number of commits continues to grow. This study is expected to provide suggestions for the successful performance of open collaboration projects in the future.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.107-120
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• 2014

This study proposes a novel recommender system using the structural hole analysis to reflect qualitative and emotional information in recommendation process. Although collaborative filtering (CF) is known as the most popular recommendation algorithm, it has some limitations including scalability and sparsity problems. The scalability problem arises when the volume of users and items become quite large. It means that CF cannot scale up due to large computation time for finding neighbors from the user-item matrix as the number of users and items increases in real-world e-commerce sites. Sparsity is a common problem of most recommender systems due to the fact that users generally evaluate only a small portion of the whole items. In addition, the cold-start problem is the special case of the sparsity problem when users or items newly added to the system with no ratings at all. When the user's preference evaluation data is sparse, two users or items are unlikely to have common ratings, and finally, CF will predict ratings using a very limited number of similar users. Moreover, it may produces biased recommendations because similarity weights may be estimated using only a small portion of rating data. In this study, we suggest a novel limitation of the conventional CF. The limitation is that CF does not consider qualitative and emotional information about users in the recommendation process because it only utilizes user's preference scores of the user-item matrix. To address this novel limitation, this study proposes cluster-indexing CF model with the structural hole analysis for recommendations. In general, the structural hole means a location which connects two separate actors without any redundant connections in the network. The actor who occupies the structural hole can easily access to non-redundant, various and fresh information. Therefore, the actor who occupies the structural hole may be a important person in the focal network and he or she may be the representative person in the focal subgroup in the network. Thus, his or her characteristics may represent the general characteristics of the users in the focal subgroup. In this sense, we can distinguish friends and strangers of the focal user utilizing the structural hole analysis. This study uses the structural hole analysis to select structural holes in subgroups as an initial seeds for a cluster analysis. First, we gather data about users' preference ratings for items and their social network information. For gathering research data, we develop a data collection system. Then, we perform structural hole analysis and find structural holes of social network. Next, we use these structural holes as cluster centroids for the clustering algorithm. Finally, this study makes recommendations using CF within user's cluster, and compare the recommendation performances of comparative models. For implementing experiments of the proposed model, we composite the experimental results from two experiments. The first experiment is the structural hole analysis. For the first one, this study employs a software package for the analysis of social network data - UCINET version 6. The second one is for performing modified clustering, and CF using the result of the cluster analysis. We develop an experimental system using VBA (Visual Basic for Application) of Microsoft Excel 2007 for the second one. This study designs to analyzing clustering based on a novel similarity measure - Pearson correlation between user preference rating vectors for the modified clustering experiment. In addition, this study uses 'all-but-one' approach for the CF experiment. In order to validate the effectiveness of our proposed model, we apply three comparative types of CF models to the same dataset. The experimental results show that the proposed model outperforms the other comparative models. In especial, the proposed model significantly performs better than two comparative modes with the cluster analysis from the statistical significance test. However, the difference between the proposed model and the naive model does not have statistical significance.

• Journal of Korea Technology Innovation Society
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• v.20 no.4
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• pp.1036-1056
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• 2017

The rapid increase of ageing population and chronic disease patients cause high medical expenses, and it led an increased attention to digital healthcare. Smart car technologies for healthcare have been developing to recognize drivers' status and predict diverse driving environments. The present study aimed to understand the research trends of autonomous vehicle technologies of Korea and the United States through time series analysis, network analysis, visualization, and comparison between the two countries. The results suggest that cooperative study needs to be done in common research areas such as driver's safety and algorithms. It is also needed to conduct studies and benchmark about liking technique related to part-to-part and vehicle-to-vehicle as America's competitive advantaged area. In the US, diverse approaches of autonomous vehicle technologies have used to consider the characteristics of various age groups and passengers' health status through sensor, while in Korea, only one aspect, older drivers, is mentioned. Implications for the development direction of autonomous vehicle technologies with competitiveness in considering public health, ethics, and driver's safety and convenience are discussed in detail.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.11
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• pp.6502-6508
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• 2014

The concept of Communities of Practice (CoPs) has been highlighted as an effective method for knowledge sharing in Knowledge Management (KM) and has been utilized strategically by many organizations. Therefore, the need to diagnose knowledge sharing activities in CoPs has increased. Previous studies of CoP strategies has generally suggested broad guidelines without diagnosing the current knowledge sharing status of individual CoPs. Furthermore, diagnosis methodologies are not connected to the strategic direction and require considerable time and effort to conduct regularly. The purpose of this paper was to develop a sustainable diagnosis framework for identifying knowledge sharing activities in virtual CoPs and to suggest strategies for CoPs-based on the proposed diagnosis framework. Finally, the proposed diagnosis framework was applied to an educational service case.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2017.05a
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• pp.873-900
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• 2017

본 연구는 사회연결망 분석이론을 통해 원자력 과학기술에 대한 사회과학 연구의 경향적 특징을 파악하고, 동 분야의 주요 연구주제와 하부 연구분야를 도출하기 위해 수행되었다. 연구대상은 1957년부터 2016년까지 국내 학술지에 게재된 원자력 관련 사회과학 분야 연구논문 605건으로, 저자가 제시한 키워드 간 관계망 형성을 통해 네트워크 분석을 수행하였다. 분석결과, 첫째 국내에서 수행된 원자력 관련 사회과학 연구의 기술통계적 특징을 확인하였다. 원자력 사회과학 연구는 1957년부터 시작되어 꾸준히 수행되어졌는데, 2011년을 기점으로 논문발표가 급격히 증가했다. 주로 법학, 행정학, 정책학, 정치학의 연구가 대학 내 연구자를 중심으로 수행되어 왔다. 원자력 관련 기술개발이 주로 정부 출연연구기관에서 수행된다는 점을 고려 했을 때, 향후 사회과학 분야에 있어 대학과 출연기관 간의 역할분담이 필요하다. 둘째, 후쿠시마 원전사고가 발생한 2011년을 기점으로 사회과학의 원자력에 관한 연구가 양적, 질적으로 본격적으로 활성화 되었다. 원자력 관련 사회과학 지식 네트워크는 2011년 이전에 비해 규모면에서 큰 차이를 보였다. 또한, 네트워크 중심성 분석결과, 후쿠시마 사고 이전 사회과학 연구자들의 연구경향은 핵비확산, 과학기술 정책, 사회수용성이었다면, 후쿠시마 이후에는 원전해체, 손해배상, 에너지믹스, 탈핵운동 등과 같은 다양한 원자력 현안으로 확대되었다. 셋째, 하부 연구분야 도출을 통해 특정 연구주제별 쏠림현상을 확인했다. 하부 네트워크 분석 결과, 제시된 9개의 하부 연구분야는 네트워크 속성 값에서 차이를 보였다. 특히, 법학 관련 연구주제가 가장 높은 밀도를 갖는 반면 지속가능 발전과 에너지 믹스 관련 연구주제의 밀도가 가장 낮게 나타났다. 본 연구는 원자력에 관련된 학자의 인식을 연구경향 분석을 통해 파악한다는 점에서 의의가 있으며, 이는 추후 원자력 관련 정책연구자 혹은 정책결정자에게 유용한 기초자료를 제공할 것이라 기대된다.