• The Journal of Bigdata
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• v.2 no.1
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• pp.71-75
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• 2017

The topic of big data has gained attention from the industry and the academics, because of the revitalization of social network services. The purpose of this study is to analyze the application cases of big data according to the categories of U-City services. The result from this study is that inside and unstructured information is more applied than outside and structured information in order to generate big data.

• The Journal of Bigdata
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• v.1 no.1
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• pp.61-66
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• 2016

With big data analysis, companies use the customized marketing strategy based on customer's information. However, because of the concerns about privacy issue and identity theft, people start erasing their personal information or changing the privacy settings on social network site. Facebook, the most used social networking site, has the feature called 'Likes' which can be used as a tool to predict user's demographic profiles, such as sex and age range. To make accurate analysis model for the study, 'Likes' data has been processed by using Gaussian RBF and nFactors for dimensionality reduction. With random Forest and 5-fold cross-validation, the result shows that sex has 75% and age has 97.85% accuracy rate. From this study, we expect to provide an useful guideline for companies and marketers who are suffering to collect customers' data.

• The Journal of Bigdata
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• v.6 no.1
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• pp.1-12
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• 2021

The rapid growth of mobile-based online shopping and the appearance of untact business initiated by COVID-19 has led to an explosive increase in demand for logistics services such as delivery services. In order to respond to the rapidly growing demand, most logistics and distribution companies are working to improve customer service levels through the establishment of a full-filament center in the city center. However, due to social factors such as high land prices and traffic congestion, it becomes more difficult to establish the logistics facilities in the city center. In this study, it has been proposed the way to choose the candidate locations for the shared distribution centers among the space nearby the tall-gate which can be idle after the smart tolling service is widely extended. In order to evaluate the candidate locations, it has been evaluated the centralities of all candidates using social network analysis (SNA). To understand the result considering the characteristics of centrality, the network structure was regenerated based on the distance and the traveling time, respectively. It is possible to refer the result of evaluation based on the cumulative relative importance to choose the best set of candidates.

• The Journal of the Korea Contents Association
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• v.16 no.11
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• pp.477-487
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• 2016

In 1995, the filming location of the drama had been famous, and as a result it brings the effect of increasing tourists of that areas. After that, many local governments try to host the filming on their regions to be potential tourist attractions. With the same stream, Jeonju also has attempted to host International Film Festival and to set up Jeonju Film Commission and Jeonju Cinema Complex. However, although the city already has rich infrastructure facilities to make films, the city hardly tries to use the filming locations as tourist attractions. This study suggests four ways of using filming locations as tourist attractions to activate Jeonju economy and improve Jeonju's cultural image. We firstly collect social bigdata related with tourists of filming locations and tourist attractions in Jeonju from Twitter, which is the most representative SNS, and then perform frequency and trend analysis. We also investigate major factors of visits to tourist's attractions based on content analysis of tweet mentions.

• The Journal of Bigdata
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• v.2 no.1
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• pp.27-39
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• 2017

The importance of online communication is getting increased by the rapid growth of smartphone supply and Social Network Service (SNS) use. Catching up with the trend, firms are actively use SNS to improve brand image, promote products, and communicate with customer. On the one hand, SNS is the channel for firms' marketing activities, but on the other, it is also the channel where the events related to the firms propagate in real time. Firms are led to unexpected state of crisis, when events are quickly spread out on SNS. Then firms are assessed their image by the way they deal with the state of crisis. This paper proposes to figure out user response on SNS according to each crisis response strategies by analyzing event-related twitter data when crisis situations of firms arise. We classify crisis response strategies into response attitude, defensive and accommodative response, and response speed, fast and slow response. This paper suggests optimal crisis response strategy to firms regarding state of crisis propagated on SNS.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1570-1594
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• 2017

Social network services have recently changed from environments for simply building connections among users to open platforms for generating and sharing various forms of information. Existing user reputation computation methods are inadequate for determining the trust in users on social media where explicit ratings are rare, because they determine the trust in users based on user profile, explicit relations, and explicit ratings. To solve this limitation of previous research, we propose a user reputation computation method suitable for the social media environment by incorporating implicit as well as explicit ratings. Reliable user reputation is estimated by identifying malicious information raters, modifying explicit ratings, and applying them to user reputation scores. The proposed method incorporates implicit ratings into user reputation estimation by differentiating positive and negative implicit ratings. Moreover, the method generates user reputation scores for individual categories to determine a given user's expertise, and incorporates the number of users who participated in rating to determine a given user's influence. This allows reputation scores to be generated also for users who have received no explicit ratings, and, thereby, is more suitable for social media. In addition, based on the user reputation scores, malicious information providers can be identified.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.01a
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• pp.317-318
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• 2014

정보기술 발전은 쇼설 네트워크 서비스로 발전하게 되었고, 정보를 공유하고 소통하면서 방대한 콘텐츠가 생성되어 빅 테이터 시대를 맞이하게 되었다. 따라서 방대한 데이터를 정보와 콘텐츠로 사용하기 위해 인포그래픽이 등장하였다. 인포그래픽은 정보들을 간결하고 효과적으로 표현해 줌으로써 가독성과 정보 제공 효과와 시간과 경제성의 효과를 나타낸다.

• The Journal of Bigdata
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• v.2 no.2
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• pp.9-16
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• 2017

Global Platform Solution Company (aka Amazon, Google, MS, IBM) who has cloud platform, are driving AI and Big Data service on their cloud platform. It will dramatically change Enterprise business value chain and infrastructures in Supply Chain Management, Enterprise Resource Planning in Customer relationship Management. Enterprise are focusing the channel with customers and Business Partners and also changing their infrastructures to platform by integrating data. It will be Digital Transformation for decision support. AI and Deep learning technology are rapidly combined to their data driven platform, which supports mobile, social and big data. The collaboration of platform service with business partner and the customer will generate new ecosystem market and it will be the new way of enterprise revolution as a part of the 4th industrial revolution.

• The Journal of Bigdata
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• v.2 no.2
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• pp.57-66
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• 2017

Companies has been investing enormous resources in promotion as the market keeps changing rapidly. Therefore, there are growing needs to measure the impact of a promotion on revenue growth. To investigate the effect of promotion in franchise food service industry, this study empirically analyzed text data from Twitter, one of the dominant social network services. Our findings show that a gap between promotions, promotion duration, and season have a significant influence on a volume of twitter buzz, which represents a promotion effect in our study. Next, we tried to analyze the reason why those factors were related to the promotion effect. Finally, we suggested promotion strategies related to each influential factor depending on types of business in food service industry.

• The Journal of Bigdata
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• v.1 no.1
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• pp.33-39
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• 2016

Recently, predicting personality with the help of smartphone usage becomes very interesting and attention grabbing topic in the field of research. At present there are some approaches towards detecting a user's personality which uses the smartphones usage data, such as call detail records (CDRs), the usage of short message services (SMSs) and the usage of social networking services application. In this paper, we focus on the assessing the correlation between MBTI based user personality and the smartphone usage data. We used $Na{\ddot{i}}ve$ Bayes and SVM classifier for classifying user personalities by extracting some features from smartphone usage data. From analysis it is observed that, among all extracted features facebook usage log working as the best feature for classification of introverts and extraverts; and SVM classifier works well as compared to $Na{\ddot{i}}ve$ Bayes.