• Korean Journal of Social Welfare
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• v.64 no.2
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• pp.159-183
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• 2012

This study intends to explore structural characteristics of social networks for female immigrants married to Korean men, and to analyze the relationship between the characteristics and types of social supports provided by their social networks and the differences between support-giving and support-receiving networks. Ego-centered network analysis is used for collecting network data on fifty-three migrant wives selected by a snowball sampling method. Results show that social support receiving and giving networks of female immigrants have similarities rather than differences, which implied that they play roles not only as support receivers, but also as support givers in their social networks. Also the study suggests that there are correlations between networks' characteristics, such as density and effective size of ego network, and types of supports. The result indicates that the less cohesive and less redundant ties female immigrants had, the more diverse and more informational and emotional supports they obtained from their social networks. Due to the sampling method and size, this study has a limitation to generalize the results for the whole population of female immigrants in Korea. However, it provides a basic understanding of female immigrants' social networks.

• Journal of the Korea Safety Management & Science
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• v.14 no.4
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• pp.229-238
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• 2012

Within last 10 years, internet has become a daily activity, and humankind had to face the Data Deluge, a dramatic increase of digital data (Economist 2012). Due to exponential increase in amount of digital data, large scale data has become a big issue and hence the term 'big data' appeared. There is no official agreement in quantitative and detailed definition of the 'big data', but the meaning is expanding to its value and efficacy. Big data not only has the standardized personal information (internal) like customer information, but also has complex data of external, atypical, social, and real time data. Big data's technology has the concept that covers wide range technology, including 'data achievement, save/manage, analysis, and application'. To define the connected technology of 'big data', there are Big Table, Cassandra, Hadoop, MapReduce, Hbase, and NoSQL, and for the sub-techniques, Text Mining, Opinion Mining, Social Network Analysis, Cluster Analysis are gaining attention. The three features that 'bid data' needs to have is about creating large amounts of individual elements (high-resolution) to variety of high-frequency data. Big data has three defining features of volume, variety, and velocity, which is called the '3V'. There is increase in complexity as the 4th feature, and as all 4features are satisfied, it becomes more suitable to a 'big data'. In this study, we have looked at various reasons why companies need to impose 'big data', ways of application, and advanced cases of domestic and foreign applications. To correspond effectively to 'big data' revolution, paradigm shift in areas of data production, distribution, and consumption is needed, and insight of unfolding and preparing future business by considering the unpredictable market of technology, industry environment, and flow of social demand is desperately needed.

• Journal of Service Research and Studies
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• v.6 no.4
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• pp.15-34
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• 2016

The purpose of this study is to examine the effects of social entrepreneurship on organizational performance and to identify network moderating effects on social entrepreneurship and organizational performance. Data collected from 189 employees working in social enterprises were analyzed by using SPSS 18.0 program. Results are as follows: First, innovativeness, proactiveness, and social value orientation which were sub-factors of social entrepreneurship had positive effects on social performance. Second, proactiveness, risk-taking, and social value orientation which were sub-factors of social entrepreneurship had positive effects on economic performance. Third, the network significantly moderated the effects on innovativeness, proactiveness, social value orientation which were sub-factors of social entrepreneurship, and social performance. Fourth, the network significantly moderated the effects on proactiveness which was a sub-factor of social entrepreneurship, and economic performance. These results exhibited that the social entrepreneurship was effective to increase the organizational performance by activating the network and proposed a useful approach for further studies as an investigation method.

• Convergence Security Journal
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• v.14 no.4
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• pp.33-40
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• 2014

Since 2006, social networking services such as Facebook, Twitter, and Blog user increasing very rapidly. Furthermore demand of Book Retrieval Service using smartphone on social network service environment are increasing too. This service can to easy and share information for search book and data in several university. However, the current edition of the social services in the country to provide security services do not have the right. Therefore, we suggest a social book Retrieval service in social network environment that can support user authentication and partial filter search method on smartphone. our proposed system can to provide more speed responsiveness, effective display result on smartphone and security service.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.4
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• pp.109-120
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• 2021

Despite the growing online exposure of retailes, offline retail channels still outperform online channels in the total retail volume of some countries. There is much interest in the physical layout plans of retail stores to expand sales. Product placement that have a large impact on customer purchasing behavior at offline retailers influences customer movement and sales volume. But in many cases, each retailer relies on unsystematic and autonomous product placement. When multiple products are sold with one purchase, the customer's movement for shopping may be evaluated in terms of customer efficiency and additional impulse purchase. In this paper, the social network is applied to sales data of a retail store and the result is used for evaluation of product placement and customer path. The frequent sales product composition was identified using k-core from sales data in the form of shopping baskets. The location was checked for the identified compositions of products, the spatial variance was measured and the customer's path was identified. With these results, the store arrangement of products was evaluated with appropriate improvement directions. The analysis method of this paper can be an alternative analysis approach for better layout of retail stores.

• Journal of the Korean Society of Clothing and Textiles
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• v.44 no.5
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• pp.1019-1035
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• 2020

This study examines the millennial generation, who express themselves and share information on social media after experiencing constantly changing 'hot places' (places of interest) in contemporary cities, with the goal of analyzing space consumption behaviors. Data were collected via an Instagram crawler application developed with Python 3.4 administered to 19,262 posts using the term 'hot places' from November 1 and December 15, 2019. Issues were derived from a text mining technique using Textom 2.0; in addition, semantic network analysis using Ucinet6 and the NetDraw program were also conducted. The results are as follows. First, a frequency analysis of keywords for hot places indicated words frequently found in nouns were related to food, local names, SNS and timing. Words related to positive emotions felt in experience, and words related to behavior in hot places appeared in predicate. Based on importance, communication is the most important keyword and influenced all issues. Second, the results of visualization of semantic network analysis revealed four categories in the scope of the definition of "hot place": (1) culinary exploration, (2) atmosphere of cafés, (3) happy daily life of 'me' expressed in images, (4) emotional photos.

• The Journal of Bigdata
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• v.4 no.1
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• pp.99-118
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• 2019

This study used network analysis and clustering techniques to analyze studies on social media participation. As a result of the main path analysis, 37 major studies were extracted and divided into two networks: community-related networks and new media-related. Network analysis and clustering result in four clusters. This study has the academic significance of using academic data to grasp research trends at a macro level and using network analysis and machine learning as a methodology.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.719-728
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• 2016

In this paper, we analyzed IoT patent data using the social network analysis of keyword community network in patents related to Internet of Things technology. To identify the difference of IoT patent trends between Korea and USA, 100 Korea patents and 100 USA patents were collected, respectively. First, we first extracted important keywords from IoT patent abstracts using the TF-IDF weight and their correlation and then constructed the keyword network based on the selected keywords. Second, we constructed a keyword community network based on the keyword community and performed social network analysis. Our experimental results showed while Korea patents focus on the core technologies of IoT (such as security, semiconductors and image process areas), USA patents focus on the applications of IoT (such as the smart home, interactive media and telecommunications).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3868-3888
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• 2022

A widely used social networking service like Twitter has the ability to disseminate information to large groups of people even during a pandemic. At the same time, it is a convenient medium to share irrelevant and unverified information online and poses a potential threat to society. In this research, conventional machine learning algorithms are analyzed to classify the data as either non-rumor data or rumor data. Machine learning techniques have limited tuning capability and make decisions based on their learning. To tackle this problem the authors propose a deep learning-based Rumor Detection Neural Network model to predict the rumor tweet in real-world events. This model comprises three layers, AttCNN layer is used to extract local and position invariant features from the data, AttBi-LSTM layer to extract important semantic or contextual information and HPOOL to combine the down sampling patches of the input feature maps from the average and maximum pooling layers. A dataset from Kaggle and ground dataset #gaja are used to train the proposed Rumor Detection Neural Network to determine the veracity of the rumor. The experimental results of the RDNN Classifier demonstrate an accuracy of 93.24% and 95.41% in identifying rumor tweets in real-time events.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.544-547
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• 2004

Current increasingly information systems log historic information in a systematic way. Not only workflow management systems, but also ERP, CRM, SCM, and B2B systems often provide a so-called 'event log'. Unfortunately, the information in these event logs is rarely used to analyze the underlying processes. Process mining aims at improving this problem by providing techniques and tools for discovering process, control, data, organizational, and social structures from event logs. This paper focuses on the mining social networks. This is possible because event logs typically record information about the users executing the activities recorded in the log. To do this we combine concepts from workflow management and social network analysis. This paper introduces the approach and presents a tool to mine social networks from event logs.