• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.59-68
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• 2022

As real-time live streaming broadcasting and non-face-to-face classes are spreading in the Corona era, it is time to take academic interest in online streaming classes. In particular, it is important to clarify why users use online streaming classes. Therefore, this study proposes social presence, interest, convenience of use, and interactivity as characteristics of online streaming classes, and aims to verify how these characteristics affect learning satisfaction and furthermore, recommendation intention. As a result of conducting a survey on 338 Chinese collegestudents, it was found that interactivity, social presence, and interest had a positive effect on learning satisfaction, but the effect of ease did not appear. On the other hand, it was confirmed that learning satisfaction had a positive effect on the online streaming class recommendation intention.

• KNOM Review
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• v.24 no.1
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• pp.20-28
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• 2021

Extensive use of social media applications and mobile devices continues to increase data traffic. Social media applications generate an endless and massive amount of multimedia traffic, specifically video traffic. Many social media platforms such as YouTube, Daily Motion, and Netflix generate endless video traffic. On these platforms, only a few popular videos are requested many times as compared to other videos. These popular videos should be cached in the user vicinity to meet continuous user demands. MEC has emerged as an essential paradigm for handling consistent user demand and caching videos in user proximity. The problem is to understand how user demand pattern varies with time. This paper analyzes three publicly available datasets, MovieLens 20M, MovieLens 100K, and The Movies Dataset, to find the user request pattern over time. We find hourly, daily, monthly, and yearly trends of all the datasets. Our resulted pattern could be used in other research while generating and analyzing the user request pattern in MEC-based video caching scenarios.

• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.935-945
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• 2021

The purpose of this study is to analyze the social perception of Arduino lecture using big data analysis method. For this purpose, data from January 2012 to May 2021 were collected using the Textom website as a keyword searched for 'arduino + lecture' in blogs, cafes, and news channels of NAVER website. The collected data was refined using the Textom website, and text mining analysis and semantic network analysis were performed by opening the Textom website, Ucinet 6, and Netdraw programs. As a result of text mining analysis such as frequency analysis, TF-IDF analysis, and degree centrality it was confirmed that 'education' and 'coding' were the top keywords. As a result of CONCOR analysis for semantic network analysis, four clusters can be identified: 'Arduino-related education', 'Physical computing-related lecture', 'Arduino special lecture', and 'GUI programming'. Through this study, it was possible to confirm various meaningful social perceptions of the general public in relation to Arduino lecture on the Internet. The results of this study will be used as data that provides meaningful implications for instructors preparing for Arduino lectures, researchers studying the subject, and policy makers who establish software education or coding education and related policies.

• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.515-520
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• 2022

Metaverse is at the center of heated debates in many areas recently. Coupled with real world, metaverse is extending its domain into social and cultural activities in addition to economic value creation as untact activities increase. Global companies are investing in R&D for metaverse. The reason is that metaverse is supposed to create new value by converging virtual and real worlds thanks to technology advancement such as AI, big data, 3D graphic, 5G, cloud computing, etc. Thus, innovative changes are expected in the economic, social and cultural areas. However, there are many problems to be solved yet for connecting virtual world and real one. Also, epoch-making development of products and services should be done for realistic experience and profit creation using virtual space in various industries beyond untact social activities against pandemic situation. The essence, present condition, development and its application areas of metaverse will be analyzed, and expected problems researched so that the strategy and methodology for securing global competitiveness will be addressed in coming metaverse era.

• Journal of Information Science Theory and Practice
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• v.12 no.1
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• pp.1-20
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• 2024

This research aims to study and confirm enabling factors affecting the knowledge transfer and business process of community enterprise groups in Pattani province, Thailand. Key informants were community enterprise entrepreneurs; 30 people were selected purposively with criteria. This study used a mixed-methods approach and conducted semi-structured interviews to collect data. Qualitative data were analyzed using content analysis and classification, while quantitative data were analyzed using descriptive statistics with frequency, percentage, mean, and standard deviation. Moreover, inferential statistics chi-square value, Phi Cramer's V, and multiple regression analysis with the R program for statistical computing were employed to analyze the relationship between the variables, test the research hypothesis, and create forecasting equations. The research results revealed that the overview of enabling factors had a very high relationship (Cramer's V=0.965). Regarding community enterprise, it was found that enabling factors related to the knowledge transfer and business process consisted of four factors: regulations and administrative guidelines, business plan, reinforcement, and brainstorming. Reinforcement was the factor with the highest degree of correlation (Cramer's V=0.873) and predictor of influence on the knowledge transfer and business process (R₂=0.670, p<0.05). This study's findings can lead to the developing of guidelines for promoting community enterprises properly and timely. These guidelines are expected to be used to develop knowledge about business models for community enterprises, which will help to improve their competency and competitiveness.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.69-78
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• 2024

Recently, consumers have shown an increasing tendency to seek information related to environmental, social, and governance (ESG) aspects in order to choose products with higher social value and environmental friendliness. In this paper, we proposes a product recommendation system applying ESG indicators tailored to the recent consumer trend of value-based consumption, utilizing a model called MultiSAGE that combines GraphSAGE and GAT. To achieve this, ESG rating data for 1,033 companies in 2022 collected from the Korea ESG Standard Institute and actual product data from N companies were transformed into a Heterogeneous Graph format through a data processing pipeline. The MultiSAGE model was then applied in machine learning to implement a recommendation system that, given a specific product, suggests eco-friendly alternatives. The implementation results indicate that consumers can easily compare and purchase products with ESG indicators applied, and it is anticipated that this system will be utilized in recommending products with social value and environmental friendliness.

• Journal of the HCI Society of Korea
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• v.1 no.2
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• pp.35-42
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• 2006

The importance of interface agent as user interface increases in the ubiquitous computing environment. It is essential that an interface agent can develop social relationship with users. We propose that self-disclosure, a major factor to form and maintain human relationship, can be useful to achieve this goal. This study examined the effects of the degree of a computer agent's self-disclosure on the users' social responses. The experiment was conducted in a 2(intimacy of agent's disclosure: high vs. low) by 2(amount of agent's disclosure: high vs. low) between-group design. The results show that: 1) reciprocity of self-disclosure was found in both sub-dimensions (intimacy and amount) of self-disclosure; 2) in case that participants received highly intimate self-disclosure from the agent, social attraction, trustworthiness and expectation of mutual influence toward agent were lower than when the agent's disclosure was less intimate. These findings suggest that the intimacy of agent's self-disclosure can affect on gathering user information and human-agent relationship formation separately. While agent's highly intimate disclosure can be useful to gather user information, agent's appropriate disclosure can be useful to form positive user-agent relationship.

• Journal of Intelligence and Information Systems
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• v.17 no.2
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• pp.39-60
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• 2011

Since the introduction of web 2.0 technology, social network service has been recognized as the foundation of an important future information technology. The advent of web 2.0 has led to the change of content creators. In the existing web, content creators are service providers, whereas they have changed into service users in the recent web. Users share experiences with other users improving contents quality, thereby it has increased the importance of social network. As a result, diverse forms of social network service have been emerged from relations and experiences of users. Social network is a network to construct and express social relations among people who share interests and activities. Today's social network service has not merely confined itself to showing user interactions, but it has also developed into a level in which content generation and evaluation are interacting with each other. As the volume of contents generated from social network service and the number of connections between users have drastically increased, the social network extraction method becomes more complicated. Consequently the following problems for the social network extraction arise. First problem lies in insufficiency of representational power of object in the social network. Second problem is incapability of expressional power in the diverse connections among users. Third problem is the difficulty of creating dynamic change in the social network due to change in user interests. And lastly, lack of method capable of integrating and processing data efficiently in the heterogeneous distributed computing environment. The first and last problems can be solved by using FOAF, a tool for describing ontology-based user profiles for construction of social network. However, solving second and third problems require a novel technology to reflect dynamic change of user interests and relations. In this paper, we propose a novel method to overcome the above problems of existing social network extraction method by applying FOAF (a tool for describing user profiles) and RSS (a literary web work publishing mechanism) to OLAP system in order to dynamically innovate and manage FOAF. We employed data interoperability which is an important characteristic of FOAF in this paper. Next we used RSS to reflect such changes as time flow and user interests. RSS, a tool for literary web work, provides standard vocabulary for distribution at web sites and contents in the form of RDF/XML. In this paper, we collect personal information and relations of users by utilizing FOAF. We also collect user contents by utilizing RSS. Finally, collected data is inserted into the database by star schema. The system we proposed in this paper generates OLAP cube using data in the database. 'Dynamic FOAF Management Algorithm' processes generated OLAP cube. Dynamic FOAF Management Algorithm consists of two functions: one is find_id_interest() and the other is find_relation (). Find_id_interest() is used to extract user interests during the input period, and find-relation() extracts users matching user interests. Finally, the proposed system reconstructs FOAF by reflecting extracted relationships and interests of users. For the justification of the suggested idea, we showed the implemented result together with its analysis. We used C# language and MS-SQL database, and input FOAF and RSS as data collected from livejournal.com. The implemented result shows that foaf : interest of users has reached an average of 19 percent increase for four weeks. In proportion to the increased foaf : interest change, the number of foaf : knows of users has grown an average of 9 percent for four weeks. As we use FOAF and RSS as basic data which have a wide support in web 2.0 and social network service, we have a definite advantage in utilizing user data distributed in the diverse web sites and services regardless of language and types of computer. By using suggested method in this paper, we can provide better services coping with the rapid change of user interests with the automatic application of FOAF.

• ETRI Journal
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• v.38 no.4
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• pp.714-723
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• 2016

Considerable attention has been given to processing graph data in recent years. An efficient method for computing the node proximity is one of the most challenging problems for many applications such as recommendation systems and social networks. Regarding large-scale, mutable datasets and user queries, top-k query processing has gained significant interest. This paper presents a novel method to find top-k answers in a node proximity search based on the well-known measure, Personalized PageRank (PPR). First, we introduce a distribution state transition graph (DSTG) to depict iterative steps for solving the PPR equation. Second, we propose a weight distribution model of a DSTG to capture the states of intermediate PPR scores and their distribution. Using a DSTG, we can selectively follow and compare multiple random paths with different lengths to find the most promising nodes. Moreover, we prove that the results of our method are equivalent to the PPR results. Comparative performance studies using two real datasets clearly show that our method is practical and accurate.

• Electronics and Telecommunications Trends
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• v.35 no.6
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• pp.12-23
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• 2020

Because of economic polarization and difficulties, extreme personalization, and the complexity of social relationships, modern people are experiencing various mental disorders or pathologies. Accordingly, there is an urgent need to prepare more active countermeasures and support those with mental health difficulties to improve mental health and prevent abnormal pathologies. Artificial intelligence (AI) is expected to improve the mental health of individuals through emotional enhancement beyond affective computing. We investigated how to use AI to prevent and diagnose mental diseases or disorders, support treatment, and manage followup. In particular, promising services that can be used in daily life or medical clinics were discovered and active directions for realizing these services are suggested.