• Journal of Digital Convergence
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• v.17 no.11
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• pp.39-51
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• 2019

This study attempts to investigates big data of marital relationships of Kimhae Kim's family on their genealogy. Through the network analysis, how the relationship between families have been structured and changed longitudinally from 1500s to 1800s. Results showed that the network sizes had increased and centralizations had decreased gradually. However, the results indicated that some families were stably located in the central position on the networks. This study suggests that data on genealogy can be used for big data and social network analyses.

• The Journal of Society for e-Business Studies
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• v.18 no.4
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• pp.315-326
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• 2013

Users in social media post various types of opinions such as product reviews and movie reviews. It is a common trend that customers get assistance from the opinions in making their decisions. However, as opinion usage grows, distorted feedbacks also have increased. For example, exaggerated positive opinions are posted for promoting target products. So are negative opinions which are far from common evaluations. Finding these biased opinions becomes important to keep social media reliable. Techniques of opinion mining (or sentiment analysis) have been developed to determine sentiment polarity of opinionated documents. These techniques can be utilized for finding the biased opinions. However, the previous techniques have some drawback. They categorize the text into only positive and negative, and they also need a large amount of training data to build the classifier. In this paper, we propose methods for discovering the biased opinions which are skewed from the overall common opinions. The methods are based on angle based outlier detection and personalized PageRank, which can be applied without training data. We analyze the performance of the proposed techniques by presenting experimental results on a movie review dataset.

• Journal of Korean Society of Forest Science
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• v.108 no.1
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• pp.97-108
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• 2019

In a situation where production of short-term income forestry products has been stagnant or decreased in recent years, the government or related agencies are trying to promote consumption of short-term income forest products. While consumer sentiment studies on short-term income forestry are being conducted as part of efforts to encourage consumption, most of the studies rely solely on a survey-based method. In the information age, consumer sentiment toward consumer goods is reflected mostly on social networking sites due to the spread of the Internet. It is necessary to avoid relying solely on a survey-based method in existing research and directly analyze social networking sites that reflect consumers' wishes. In response, this study identified consumer preferences for major short-term income forest products through social big data analyses and used the results to establish strategies for promoting the sale of short-term income forest products. This paper is different from previous research using only a survey-based method, and it uses SNS to understand consumer preferences. The results of this study are expected to directly help the government or related agencies promote consumption of short-term income forest products and, ultimately, help improve forest-related income and promote healthy forest condition.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.111-119
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• 2022

The purpose of this paper is to examine the change in perception of 3D virtual fashion before and after COVID-19 using big data analysis. The data collection period is from January 1, 2017, before the outbreak of COVID-19, to October 30, 2022, after the outbreak. Big data was collected for key words related to 3D virtual fashion extracted from social media such as Naver, Daum, Google, and YouTube using Textom. After the collected words were refined, word cloud, word frequency, connection centrality, network visualization, and CONCOR analysis were performed. As a result of extracting and analyzing 32,461 words with 3D virtual fashion as a keyword, the frequency and centrality of fashion, virtual, and technology appeared the highest, and the frequency of appearance of digital, design, clothing, utilization, and manufacturing was also high. Through this, it was found that 3D virtual fashion is being used throughout the industry along with the development of technology. In particular, the key words that stand out the most after COVID-19 are metaverse and 3D education, which are in high demand in the fashion industry.

• The Journal of Society for e-Business Studies
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• v.18 no.1
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• pp.107-127
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• 2013

The evolution of IT facilitated the communication and knowledge sharing between the social network service (SNS) users. When the more information about SNS users had been posted in SNS site, SNS users had sometimes exposed in the risk of privacy invasion. To remedy this problem, we had introduced the information control mechanisms from the prior studies in data management to the SNS area and empirically validated the effect of these mechanisms in this research. Three information control mechanisms had been elected as access control, reference control and diffusion control. We had conducted a survey to the Facebook users which is the most famous SNS site. 459 data had been gathered and analyzed by PLS algorism. As the results, reference control and diffusion control has significantly increased the trust on SNS providers and decrease the privacy concern. This change could significantly affect on the satisfaction with the SNS site and knowledge sharing intention of SNS users. This study could introduce the new perspective about privacy protection issues in SNS area. Also, the information control mechanisms suggested in this study could contribute to make more robust privacy protection mechanisms in SNS site in practice.

• Journal of the Korean Regional Science Association
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• v.39 no.1
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• pp.3-20
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• 2023

This study aims to categorize the 14 major gentrified commercial areas of Seoul and analyze their characteristics based on their sense of place. To achieve this, we conducted hierarchical cluster analysis using text data collected from Naver Blog. We divided the districts into two dimensions: "experience" and "feature" and analyzed their characteristics using LDA (Latent Dirichlet Allocation) of the text data and statistical data collected from Seoul Open Data Square. As a result, we classified the commercial districts of Seoul into 5 categories: 'theater district,' 'traditional cultural district,' 'female-beauty district,' 'exclusive restaurant and medical district,' and 'trend-leading district.' The findings of this study are expected to provide valuable insights for policy-makers to develop more efficient and suitable commercial policies.

• Journal of the Korean Society for information Management
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• v.32 no.3
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• pp.397-412
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• 2015

The main goal of this study is to investigate how to route a question to some relevant users who have interest in the topic of the question based on users' topic interest. In order to assess users' topic interest, archived question-answer pairs in the community were used to identify latent topics in the chosen categories using LDA. Then, these topic models were used to identify users' topic interest. Furthermore, the topics of newly submitted questions were analyzed using the topic models in order to recommend relevant answerers to the question. This study introduces the process of topic modeling to investigate relevant users based on their topic interest.

• The Journal of the Korea Contents Association
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• v.16 no.6
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• pp.205-214
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• 2016

As a large amount of graph-structured data is widely used in various applications such as social networks, semantic web, and bio-informatics, keyword-based search over graph data has been getting a lot of attention. In this paper, we propose an efficient method for keyword search over graph data to find a set of top-k answers that are relevant as well as non-redundant in structure. We define a non-redundant answer structure for a keyword query and a relevance measure for the answer. We suggest a new indexing scheme on the relevant paths between nodes and keyword terms in the graph, and also propose a query processing algorithm to find top-k non-redundant answers efficiently by exploiting the pre-calculated indexes. We present effectiveness and efficiency of the proposed approach compared to the previous method by conducting an experiment using a real dataset.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.5
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• pp.803-813
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• 2013

As the developments of smart devices and social network services, the amount of data has been exploding. The world is facing Big data era. For these reasons, the Big data processing technology which is a new technology that can handle such data has attracted much attention. One of the most representative technologies is Hadoop. Hadoop Distributed File System(HDFS) designed to run on commercial Linux server is an open source framework and can store many terabytes of data. The initial version of Hadoop did not consider security because it only focused on efficient Big data processing. As the number of users rapidly increases, a lot of sensitive data including personal information were stored on HDFS. So Hadoop announced a new version that introduces Kerberos and token system in 2009. However, this system is vulnerable to the replay attack, impersonation attack and other attacks. In this paper, we analyze these vulnerabilities of HDFS security and propose a new protocol which complements these vulnerabilities and maintains the performance of Hadoop.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1460-1465
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• 2016

Lots of costs threaten the sustainability of the national health-guarantee system. Despite research by the national center for disease control and prevention on health care dynamics with its auditing systems, there are still restrictions of time limitation, sample limitation, and, target diseases limitation. Against this backdrop, using huge volume of total data, many technologies could be fully adopted to the preliminary forecasting and its target-disease expanding of health. With structured data from the national health insurance and unstructured data from the social network service, we attempted to design a model to predict disease. The model can enhance national health and maximize social benefit by providing a health warning service. Also, the model can reduce the advent increase of national health cost and predict timely disease occurrence based on Big Data analysis. We researched related medical prediction cases and performed an experiment with a pilot project so as to verify the proposed model.