• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.8
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• pp.69-76
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• 2016

As the number of SNS users increases, so does their self-disclosure. This study examined the factors affecting self-disclosure based on Social Capital Theory and Regulatory Focus Theory. The (extent of self-disclosure by users/number of users disclosing themselves) in SNSs is expected to differ depending on their social capital (bonding capital vs. bridging capital) and regulatory focus (promotional vs. defensive). As a result of this study, it is found that bridging capital is positively related to self-disclosure in profile and in conversation, while bonding capital is positively related to self-disclosure only in conversation. With regard to regulatory focus, promotional orientation has a significant effect on self-disclosure in profile and in conversation, while defensive orientation is negatively related to self-disclosure in profile, but not related to self-disclosure in conversation. Promotional orientation is found to moderate the effect of bridging capital on self-disclosure.

• Journal of KIISE
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• v.42 no.4
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• pp.504-511
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• 2015

Recently, the amount of data is rapidly increasing with the popularity of the SNS and the development of mobile technology. So, it has been actively studied for the effective data analysis schemes of the large amounts of data. One of the typical schemes is a Voronoi diagram based on kNN join algorithm (VkNN-join) using MapReduce. For two datasets R and S, VkNN-join can reduce the time of the join query processing involving big data because it selects the corresponding subset Sj for each Ri and processes the query with them. However, VkNN-join requires a high computational cost for constructing the Voronoi diagram. Moreover, the computational overhead of the VkNN-join is high because the number of the candidate cells increases as the value of the k increases. In order to solve these problems, we propose a MapReduce-based kNN-join query processing algorithm for analyzing the large amounts of data. Using the seed-based dynamic partitioning, our algorithm can reduce the overhead for constructing the index structure. Also, it can reduce the computational overhead to find the candidate partitions by selecting corresponding partitions with the average distance between two seeds. We show that our algorithm has better performance than the existing scheme in terms of the query processing time.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.9
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• pp.603-614
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• 2013

The popularity of mobile devices provides their users with a circumstance that services and information can be accessed wherever and whenever users need. Accordingly, various studies have been proposed personalized methods to improve accessibility of mobile users to information. However, since these personalized methods require users' private information, they gives rise to problems on security. An efficient way to resolve security problems is to estimate user information by using their online and offline behavior. In this paper, for this purpose, it is proposed a novel user information identification system that identifies users' personal and social information by using both his/her behavior on social network services and proximity patterns obtained from GPS data. In the proposed system, personal information of a user like age, gender, and so on is estimated by analyzing SNS texts and POI (Point of Interest) patterns, while social information between a pair of users like family and friend is predicted with proximity patterns between the users. Each identification module is efficiently designed to handle the characteristics of user data like much noise in SNS texts and missing signals in GPS data. In experiments to evaluate the proposed system, our system shows its superiority against ordinary identification methods. This result means that the proposed system can efficiently reflect the characteristics of user data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.12
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• pp.286-299
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• 2017

The Korean performing arts has been facing stagnation due to oversupply, lack of effective distribution system, and insufficient business models. In order to overcome these difficulties, it is necessary to improve the efficiency and accuracy of marketing by using more objective market data, and to secure audience development and loyalty. This study considers the viewpoint that 'Big Data' could provide more general and accurate statistics and could ultimately promote tailoring services for performances. We examine the first case of Big Data analysis conducted by a credit card company as well as Big Data's characteristics, analytical techniques, and the theoretical background of performing arts consumer analysis. The purpose of this study is to identify the meaning and limitations of the analysis case on performing arts by Big Data and to overcome these limitations. As a result of the case study, incompleteness of credit card data for performance buyers, limits of verification of existing theory, low utilization, consumer propensity and limit of analysis of purchase driver were derived. In addition, as a solution to overcome these problems, it is possible to identify genre and performances, and to collect qualitative information, such as prospectors information, that can identify trends and purchase factors.combination with surveys, and purchase motives through mashups with social data. This research is ultimately the starting point of how the study of performing arts consumers should be done in the Big Data era and what changes should be sought. Based on our research results, we expect more concrete qualitative analysis cases for the development of audiences, and continue developing solutions for Big Data analysis and processing that accurately represent the performing arts market.

• Journal of Digital Convergence
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• v.15 no.5
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• pp.27-36
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• 2017

SNS has been emerged as an effective educational tool in college and many studies on various teaching models and methodologies have been made in order to utilize SNS in education. The purpose of this paper is to empirically investigate the effect of affecting factors of SNS on learner's attitude, intention to re-use and performance in converging college educational environment. Self-efficacy on media usage, educational expectancy, subjective norm, habit, and enjoyment were identified as affecting factors based on prior researches. An empirical analysis was attempted by survey targeting college students. The results of structural equation model using Smart PLS shows that educational expectancy, subjective norm, and enjoyment are significantly related to the learner's attitude on use of SNS in college education, but Self-efficacy on media usage and habit are not. Learner's attitude on SNS in college education was found to be significantly related to the intention to continuous use and performance. This study implicates that using SNS in university class makes learner's attitude positively and finally lead to good performance. The analysis results can provide a guideline of effective strategy for SNS utilization in college education.

• Journal of Internet Computing and Services
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• v.17 no.3
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• pp.107-113
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• 2016

Following the recent advancement in the use of social networks, a vast amount of different online reviews is created. These variable online reviews which provide feedback data of contents' are being used as sources of valuable information to both contents' users and providers. With the increasing importance of online reviews, studies on opinion mining which analyzes online reviews to extract opinions or evaluations, attitudes and emotions of the writer have been on the increase. However, previous sentiment analysis techniques of opinion-mining focus only on the classification of reviews into positive or negative classes but does not include detailed information analysis of the user's satisfaction or sentiment grounds. Also, previous designs of the sentiment analysis technique only applied to one content domain that is, either product or movie, and could not be applied to other contents from a different domain. This paper suggests a sentiment analysis technique that can analyze detailed satisfaction of online reviews and extract detailed information of the satisfaction level. The proposed technique can analyze not only one domain of contents but also a variety of contents that are not from the same domain. In addition, we design a system based on Hadoop to process vast amounts of data quickly and efficiently. Through our proposed system, both users and contents' providers will be able to receive feedback information more clearly and in detail. Consequently, potential users who will use the content can make effective decisions and contents' providers can quickly apply the users' responses when developing marketing strategy as opposed to the old methods of using surveys. Moreover, the system is expected to be used practically in various fields that require user comments.

• Informatization Policy
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• v.19 no.2
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• pp.57-84
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• 2012

If we observe the formation of communities of people and participation methods within them, we can see that those communities are developing more efficiently due to the penetration of smart phones and the growth of SNS. This trend also has an impact on information sharing activity between people and enables more active information sharing than ever before. But information sharing on SNS does not just involve the SNS users sharing information with each other. Important factors affecting the information sharing activities include the type of relationship between the users and the attitude of the information sharing individuals. Accordingly, this study selected perceived social support, perceived social influence, and perceived network structure as the factors that affect the continuous intention of people to use SNS, and performed a higher order factor analysis on those factors. Between continuous intention to use SNS and intention to share information, we selected relationship quality and information sharing behavior and executed a path analysis between the factors. We carried out an empirical analysis by utilizing SPSS 18.0 and SmartPLS 2.0 as analysis tools. Using these tools, we investigated the factors influencing continuous intention to use SNS, and tested the significance between the role and path of relationship quality and information sharing behavior between continuous intention to use SNS and intention to share information.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.5
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• pp.177-188
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• 2018

The scale of graph data has been increased rapidly because of the growth of mobile Internet applications and the proliferation of social network services. This brings upon the imminent necessity of efficient distributed and parallel graph processing approach since the size of these large-scale graphs are easily over a capacity of a single machine. Currently, there are two popular parallel graph processing approaches, vertex-centric graph processing and block centric processing. While a vertex-centric graph processing approach can easily be applied to the parallel processing system, a block-centric graph processing approach is proposed to compensate the drawbacks of the vertex-centric approach. In these systems, the initial quality of graph partition affects to the overall performance significantly. However, it is a very difficult problem to divide the graph into optimal states at the initial phase. Thus, several dynamic load balancing techniques have been studied that suggest the progressive partitioning during the graph processing time. In this paper, we present a load balancing algorithms for the block-centric graph processing approach where most of dynamic load balancing techniques are focused on vertex-centric systems. Our proposed algorithm focus on an improvement of the graph partition quality by dynamically reassigning blocks in runtime, and suggests block split strategy for escaping local optimum solution.

• The Journal of the Korea Contents Association
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• v.15 no.2
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• pp.377-392
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• 2015

In today's data-driven society, we've been hearing a great deal about the power of Big Data over the last couple of years. At the same time, it has become the most important issue that the problems is caused by the data collection, management and utilization. Moreover, Big Data has a wide applications ranging from situation awareness, decision-making to the area to enable for the foreseeable future with man-made and analysis of data. It is necessary to process data into meaningful information given that the huge amount of structured and unstructured data being created in the private and the public sector, even in disaster management. This data should be public and private sector at the same time for the appropriate linkage analysis for effective disaster management. In this paper, we conducted a literature review and case study efficient Big Data to derive the revitalization of national disaster management. The study obtained data on the role and responsibility of the public sector and the private sector to leverage Big Data for promotion of national disaster management plan. Both public and private sectors should promote common development challenges related to the openness and sharing of Big Data, technology and expansion of infrastructure, legal and institutional maintenance. The implications of the finding were discussed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.5
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• pp.1-7
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• 2020

We can build regular customer relationships combining SNS (social networking service) with shopping mall like offline trade. A customer who once purchased is registered as reaular and the relationship continues afterward. The registered regular customer get sthe information about objective product shipment and besides it, he contacts with a story of frams, growth of vegetables, sows to harvests. Consumer can purchase with one click necessary foods as he looks at timeline. Sellers give information about news. discounts to customers. Besides it, food storages, recipes can be given to consumers. The good point here is that selling and promoting can be performed within one account. This is better than link is provided for selling an promoting separately. Like this, besides personal connections using SNS, categorization function gives consumers on line shopping mall service. Once the consumer purchase, he is registered as regular. Besides, the consumers who do not know each other, can share information, suggest products, spread the news.