• Title/Summary/Keyword: Media big data

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A Study on Hotel CRM(Customer Relationship Management) using Big Data and Security (빅 데이터를 이용한 호텔기업 CRM 및 보안에 관한 연구)

  • Kong, Hyo-Soon;Song, Eun-Jee
    • Convergence Security Journal
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    • v.13 no.4
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    • pp.69-75
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    • 2013
  • Customer is the base factor of income for some corporations, so that effective CRM (Customer Relationship Management) is very important to develop the business. In order to use CRM efficiently, we should figure out customers' demands and provide services or products that the customers want. However, it is getting difficult to comprehend customers' demands because they have complicated form and getting more diverse. Recently, social media like Twitter and Facebook let customers to express their demands, and using big data is a very effective method for efficient CRM. This research suggests how to utilize big data for hotel CRM, which considers customer itself as asset of business. In addition, we discuss security problems of big data service and propose the solution for that.

BIG DATA ANALYSIS ROLE IN ADVANCING THE VARIOUS ACTIVITIES OF DIGITAL LIBRARIES: TAIBAH UNIVERSITY CASE STUDY- SAUDI ARABIA

  • Alotaibi, Saqar Moisan F
    • International Journal of Computer Science & Network Security
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    • v.21 no.8
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    • pp.297-307
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    • 2021
  • In the vibrant environment, documentation and managing systems are maintained autonomously through education foundations, book materials and libraries at the same time as information are not voluntarily accessible in a centralized location. At the moment Libraries are providing online resources and services for education activities. Moreover, libraries are applying outlets of social media such as Facebook as well as Instagrams to preview their services and procedures. Librarians with the assistance of promising tools and technology like analytics software are capable to accumulate more online information, analyse them for incorporating worth to their services. Thus Libraries can employ big data to construct enhanced decisions concerning collection developments, updating public spaces and tracking the purpose of library book materials. Big data is being produced due to library digitations and this has forced restrictions to academicians, researchers and policy creator's efforts in enhancing the quality and effectiveness. Accordingly, helping the library clients with research articles and book materials that are in line with the users interest is a big challenge and dispute based on Taibah university in Saudi Arabia. The issues of this domain brings the numerous sources of data from various institutions and sources into single place in real time which can be time consuming. The most important aim is to reduce the time that lapses among the authentic book reading and searching the specific study material.

Analyzing trends in cultural contents tourism using big data

  • Youn-hee Choi;Sang-Hak Lee;Gi-Hwan Ryu
    • International Journal of Internet, Broadcasting and Communication
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    • v.15 no.4
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    • pp.326-331
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    • 2023
  • Korea's cultural content industry can develop into another unique tourism industry. However, since other prior studies focus on the Japanese content industry, this study identifies modern industrial trends by combining the unique characteristics of Korean content, that is, cultural content tourism, and the analysis ability of big data. The current status and direction of the cultural content tourism industry were studied by utilizing the extensive information collection and in-depth analysis capabilities of big data, and as a result, it was confirmed that the trend of the cultural content industry is related to the business aspect of cultural content, not the pure content interest of cultural content. This shows that Korean cultural contents have a strong business aspect. As a limitation, when research design was conducted using social media big data, the age, gender, etc. of the subject analyzed with unique anonymity could not be known. The Korean cultural content industry is expected to be successful in terms of business.

A Study on Social Perception of Young Children with Disabilities through Social Media Big Data Analysis (소셜 미디어 빅데이터 분석을 통한 장애 유아에 대한 사회적 인식 연구)

  • Kim, Kyoung-Min
    • Journal of the Korea Convergence Society
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    • v.13 no.2
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    • pp.1-12
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    • 2022
  • The purpose of this study is to identify the social perception characteristics of young children with disabilities over the past decade. For this purpose, Textom, an Internet-based big data analysis system was used to collect data related to young children with disabilities posted on social media. 50 keywords were selected in the order of high frequency through the data cleaning process. For semantic network analysis, centrality analysis and CONCOR analysis were performed with UCINET6, and the analyzed data were visualized using NetDraw. As a result, the keywords such as 'education, needs, parents, and inclusion' ranked high in frequency, degree, and eigenvector centrality. In addition, the keywords of 'parent, teacher, problem, program, and counseling' ranked high in betweenness centrality. In CONCOR analysis, four clusters were formed centered on the keywords of 'disabilities, young child, diagnosis, and programs'. Based on these research results, the topics on social perception of young children with disabilities were investigated, and implications for each topic were discussed.

Study on the Application Methods of Big Data at a Corporation -Cases of A and Y corporation Big Data System Projects- (기업의 빅데이터 적용방안 연구 -A사, Y사 빅데이터 시스템 적용 사례-)

  • Lee, Jae Sung;Hong, Sung Chan
    • Journal of Internet Computing and Services
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    • v.15 no.1
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    • pp.103-112
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    • 2014
  • In recent years, the rapid diffusion of smart devices and growth of internet usage and social media has led to a constant production of huge amount of valuable data set that includes personal information, buying patterns, location information and other things. IT and Production Infrastructure has also started to produce its own data with the vitalization of M2M (Machine-to-Machine) and IoT (Internet of Things). This analysis study researches the applicable effects of Structured and Unstructured Big Data in various business circumstances, and purposes to find out the value creation method for a corporation through the Structured and Unstructured Big Data case studies. The result demonstrates that corporations looking for the optimized big data utilization plan could maximize their creative values by utilizing Unstructured and Structured Big Data generated interior and exterior of corporations.

Analysis for Daily Food Delivery & Consumption Trends in the Post-Covid-19 Era through Big Data

  • Jeong, Chan-u;Moon, Yoo-Jin;Hwang, Young-Ho
    • Journal of the Korea Society of Computer and Information
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    • v.26 no.1
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    • pp.231-238
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    • 2021
  • In this paper, we suggest a method of analysis for daily food delivery & consumption trends through big data of the post-Covid-19 era. Through analysis of big data and the database system, four analyzed factors, excluding weather, was proved to have significant correlation with delivery sales for 'Baedarui Minjok' of a catering delivery application. The research found that KBS, MBC and SBS Media showed remarkable results in food delivery & consumption sales soaring up to about 60 percent increase on the day after the Covid-19 related new article was issued. In addition, it proved that mobile media and web surfing were the main factors in increasing sales of food delivery & consumption applications, suggesting that viral marketing and emotional analysis by crawling data from SNS used by Millennials might be an important factor in sales growth. It can contribute the companies in the economic recession era to survive by providing the method for analyzing the big data and increasing their sales.

Consumers' perceptions of dietary supplements before and after the COVID-19 pandemic based on big data

  • Eunjung Lee;Hyo Sun Jung;Jin A Jang
    • Journal of Nutrition and Health
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    • v.56 no.3
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    • pp.330-347
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    • 2023
  • Purpose: This study identified words closely associated with the keyword "dietary supplement" (DS) using big data in Korean social media and investigated consumer perceptions and trends related to DSs before (2019) and after the coronavirus disease 2019 (COVID-19) pandemic (2021). Methods: A total of 37,313 keywords were found for the 2019 period, and 35,336 keywords were found for the 2021 period using blogs and cafes on Daum and Naver. Results were derived by text mining, semantic networking, network visualization analysis, and sentiment analysis. Results: The DS-related keywords that frequently appeared before and after COVID-19 were "recommend", "vitamin", "health", "children", "multiple", and "lactobacillus". "Calcium", "lutein", "skin", and "immunity" also had high frequency-inverse document frequency (TF-IDF) values. These keywords imply a keen interest in DSs among Korean consumers. Big data results also reflected social phenomena related to DSs; for example, "baby" and "pregnant woman" had lower TD-IDF values after the pandemic, suggesting lower marriage and birth rates but higher values for "joint", indicating reduced physical activity. A network centered on vitamins and health care was produced by semantic network analysis in 2019. In 2021, values were highest for deficiency and need, indicating that individuals were searching for DSs after the COVID-19 pandemic due to a lack an awareness of the need for adequate nutrient intake. Before the pandemic, DSs and vitamins were associated with healthcare and life cycle-related topics, such as pregnancy, but after the COVID-19 pandemic, consumer interests changed to disease prevention and treatment. Conclusion: This study provides meaningful clues regarding consumer perceptions and trends related to DSs before and after the COVID-19 pandemic and fundamental data on the effect of the pandemic on consumer interest in dietary supplements.

Model Inversion Attack: Analysis under Gray-box Scenario on Deep Learning based Face Recognition System

  • Khosravy, Mahdi;Nakamura, Kazuaki;Hirose, Yuki;Nitta, Naoko;Babaguchi, Noboru
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.15 no.3
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    • pp.1100-1118
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    • 2021
  • In a wide range of ML applications, the training data contains privacy-sensitive information that should be kept secure. Training the ML systems by privacy-sensitive data makes the ML model inherent to the data. As the structure of the model has been fine-tuned by training data, the model can be abused for accessing the data by the estimation in a reverse process called model inversion attack (MIA). Although, MIA has been applied to shallow neural network models of recognizers in literature and its threat in privacy violation has been approved, in the case of a deep learning (DL) model, its efficiency was under question. It was due to the complexity of a DL model structure, big number of DL model parameters, the huge size of training data, big number of registered users to a DL model and thereof big number of class labels. This research work first analyses the possibility of MIA on a deep learning model of a recognition system, namely a face recognizer. Second, despite the conventional MIA under the white box scenario of having partial access to the users' non-sensitive information in addition to the model structure, the MIA is implemented on a deep face recognition system by just having the model structure and parameters but not any user information. In this aspect, it is under a semi-white box scenario or in other words a gray-box scenario. The experimental results in targeting five registered users of a CNN-based face recognition system approve the possibility of regeneration of users' face images even for a deep model by MIA under a gray box scenario. Although, for some images the evaluation recognition score is low and the generated images are not easily recognizable, but for some other images the score is high and facial features of the targeted identities are observable. The objective and subjective evaluations demonstrate that privacy cyber-attack by MIA on a deep recognition system not only is feasible but also is a serious threat with increasing alert state in the future as there is considerable potential for integration more advanced ML techniques to MIA.

An Exploratory Study on the Management Framework of Social Media as Knowledge Creation Platform (지식 창조 플랫폼으로서의 소셜미디어 관리모델 설계를 위한 탐색 연구)

  • Kim, Sang Wook
    • Journal of Korea Society of Industrial Information Systems
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    • v.17 no.7
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    • pp.149-158
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    • 2012
  • Much attention is being paid to social media because of their potentials to draw collective intelligence. In this context does this study attempt to draw some implications of social media as knowledge creation platform and suggest a conceptual framework of social media management. Information sharing among the public through social media literally produces profound influence throughout the society and thus not only business firms but all levels of public institutions, including government are seeking to take its advantage for various purposes such as public relations, crowd sourcing, etc. Especially considering that social media open the possibility of social knowledge creation platform in the Big Data era, this study is perhaps able to contribute to further development of social media management model together with a series of measuring indexes.

An Youth-related Issues Analysis System Using Social Media and Big-data Mining Techniques (소셜미디어와 빅 데이터 마이닝 기술을 이용한 청소년 관련문제 분석시스템)

  • Seo, Ji Ea;Kim, Chgan Gi;Seo, Jeong Min
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2015.07a
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    • pp.93-94
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    • 2015
  • 본 논문에서는 학교 교육환경에서 청소년들에게 발생 할 수 있는소 셜미디어의 역기능을 빅 데이터 처리를 통하여 분석 할 수 있는 방법을 제시하고, 특히 악성 댓글을 위주로 한 청소년들 간의 소셜미디어를 중심으로 빅 데이터의 마이닝 기술을 활용하여 대표적인 청소년 문제의 확산을 방지 할 수 있는 시스템 제안한다.

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