• Journal of Intelligence and Information Systems
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• v.13 no.3
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• pp.119-140
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• 2007

Recommender systems enable a user to decide which information is interesting and valuable in our world of information overload. As the recent studies of distributed computing environment have been progressing actively, recommender systems, most of which were centralized, have changed toward a peer-to-peer approach. Collaborative Filtering (CF), one of the most successful technologies in recommender systems, presents several limitations, namely sparsity, scalability, cold start, and the shilling problem, in spite of its popularity. The move from centralized systems to distributed approaches can partially improve the issues; distrust of recommendation and abuses of personal information. However, distributed systems can be vulnerable to attackers, who may inject biased profiles to force systems to adapt their objectives. In this paper, we consider both effective CF in P2P environment in order to improve overall performance of system and efficient solution of the problems related to abuses of personal data and attacks of malicious users. To deal with these issues, we propose a multi-agent framework for a distributed CF focusing on the trust relationships between individuals, i.e. web of trust. We employ an agent-based approach to improve the efficiency of distributed computing and propagate trust information among users with effect. The experimental evaluation shows that the proposed method brings significant improvement in terms of the distributed computing of similarity model building and the robustness of system against malicious attacks. Finally, we are planning to study trust propagation mechanisms by taking trust decay problem into consideration.

• Journal of Technology Innovation
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• v.23 no.1
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• pp.1-36
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• 2015

The purpose of this research is to find individual user's preference for cloud storage service such as Daum Cloud, Naver N-Drive, GoogleDrive, Dropbox, SkyDrive and iCloud. Through literature reviewed and pilot tests, 6 attributes of cloud storage service (storage capacity, perceived cost, collaboration, accessibility, social influence and perceived security) were selected and all 6 attributes had significant effects on the preference of cloud storage service by conjoint analysis. The results shows that the user's willingness to pay is estimated 10,553 won for the free storage, 4,646 won for the function for mobile accessibility, and 2,443 won for more reliable cloud computing service provider. This study has significance to apply conjoint analysis with economic, technological, and environmental factors to cloud storage service (SaaS) and shed light on policy promotion of next generation of cloud computing ecosystem by user perception with willingness to pay on the storage service.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.85-93
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• 2019

Computational Science and Engineering is a convergence study that understands and solves complex problems such as science, engineering, and social phenomena through modeling using computing resources. Computational science and engineering combines algorithms, computational and informatics, and infrastructure. The importance of computational science is increasing with the improvement of computer performance and the development of large data processing technology. In Korea, Korea Institute of Science and Technology Information (KISTI) has been developing national computational science engineering software and utilization technology by combining basic science and computing technology through EDISON project. The EDISON project builds an open EDISON platform and integrates and services information systems in seven areas of computational science and engineering (computational thermal fluids, nanophysics, computational chemistry, structural dynamics, computational design, and computational medicine). Using this, we have established a web-based curriculum to lay the groundwork for fostering scientific talent and commercializing computational science and engineering software. The purpose of this study is to derive the quality characteristic factors of computational science platform and to empirically examine the effect on user satisfaction. This paper examines how the quality characteristics of information systems, the computational science engineering platform, affect the user satisfaction by modifying the research questions according to the propensity of the computational science platform by referring to the success factors of DeLone and McLean's information system. Based on the results of this study, we will suggest strategic implications for platform improvement by searching the priority of quality characteristics of computational science platform.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.12-20
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• 2016

With the enhancements in existing major industries, cloud computing-based converging services have been created, as well as value-added industries. A variety of converging services are now provided, from personalized services up to industrial services. In Korea, they have become the driving force behind existing industries throughout the whole economy, but mainly in finance, mobile systems, social computing, and home services, based on cloud services. However, not only denial of service (DOS) and distributed DOS (DDOS) attacks have occurred, but also attack techniques targeting core data in storage servers. Even security threats that are hardly detected, such as multiple attacks on a certain target, APT, and backdoor penetration have also occurred. To supplement defenses against these, in this article, a protocol for authority management is designed to provide users with safe storage services. This protocol was studied in cases of integration between a cloud environment and big data-based technology, security threats, and their requirements. Also studied were amalgamation examples and their requirements in technology-based cloud environments and big data. With the protocol suggested, based on this, security was analyzed for attack techniques that occur in the existing cloud environment, as well as big data-based techniques, in order to find improvements in session key development of approximately 55%.

• Journal of Internet Computing and Services
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• v.17 no.1
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• pp.109-118
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• 2016

As Internet technology further develops, a social networking service (SNS) also develops. But most studies on SNS are not appropriate for business purposes since they mainly focus on personal characteristics. Unlike previous studies, however, this study tries to understand the effect of SNS on performance in the perspective of business. As the result of analysis, SNSE(Social Networking Service Engagement) appears to have positive effect on TMC(Transactive Memory Capability) and PER(Performance), and TMC also seems to affect PER. On the assumption that there should be some parameters between SNSE and PER that earlier studies did not consider, this study has proved that a new way of memories, or TMC, forms the bridge between SNS and PER. It also found out that the time spent on SNS is positively controlled when SNSE affects TMC. These results are different from those of the previous studies arguing that SNS has nothing to do with PER.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.109-121
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• 2015

The purpose of this qualitative study is to explore consumer behavioral trends, psychological characteristics and various cognitive types of Millennial Generation consumers, primarily in their 20s, who are familiar with sharing economy services based on the emerging social networking technology. Using Q methodology, this paper theoretically defines four and interprets via a social science perspective four different types of these young consumers who are skilled at state-of-the-art ICT equipment, devices or online networking services. Sharing economy services in Korea's academic and industrial services are influenced by government policy, and related research is relatively new. This study is focused on discovering unique psychographic characteristics called 'schemata' that include personal interest, preference, attitude, and opinion. On the basis of 40 Q-sorted data samples, the analysis examined 180 collected statements from meta-studies and interviews with 35 individuals born between 1997 and 1992. As a result, four consumer groups were identifies: Type 1 'Early majority', Type 2 'Laggard', Type 3 'Opinion leader', and Type 4 'Late majority'. The results of this research can be used to explore to study in greater detail the behavior and psychological aspects of Millennial General consumers'.

• Journal of Internet Computing and Services
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• v.13 no.6
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• pp.9-16
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• 2012

Social Network Service (SNS) such as Twitter and Facebook has explosively grown nationwide since iPhone was introduced to Korea in 2009. In addition, KakaoStory has recently opened and joined to the SNS market, and it has grown to one of the most popular SNS in the domestic market in a short period of time. Social Network Service supports not only the formation of relationship between SNS users in common interests but also various activities such as management of personal connections and the sharing of information or contents. These three types of SNS have several common functions of sharing and distributing various contents rooted on the personal relationship formed through SNS. As each SNS user has specific reasons for the use of each service, a survey was conducted targeting those who use all of Twitter, Facebook, and KakaoStory was drawn by the statistical analyses of survey answers on users' reasons for each service. This result of study suggests factors to consider in order to exploit a new SNS or to enhance an existing service and can be used as a standard of which SNS for users to select for their own different purposes. It will also provide the basic data for the trust formation, one of the ethics in the upcoming Social Era.

• The Journal of the Korea Contents Association
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• v.13 no.2
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• pp.211-219
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• 2013

The analysis method using Big Data has evolved based on the Big data Management Technology. There are quite a few researching institutions anticipating new era in data analysis using Big Data and IT vendors has been sided with them launching standardized technologies for Big Data management technologies. Big Data is also affected by improvements of IT gadgets IT environment. Foreran by social media, analyzing method of unstructured data is being developed focusing on diversity of analyzing method, anticipation and optimization. In the past, data analyzing methods were confined to the optimization of structured data through data mining, OLAP, statics analysis. This data analysis was solely used for decision making for Chief Officers. In the new era of data analysis, however, are evolutions in various aspects of technologies; the diversity in analyzing method using new paradigm and the new data analysis experts and so forth. In addition, new patterns of data analysis will be found with the development of high performance computing environment and Big Data management techniques. Accordingly, this paper is dedicated to define the possible analyzing method of social media using Big Data. this paper is proposed practical use analysis for social media analysis through data mining analysis methodology.

• Journal of Internet Computing and Services
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• v.15 no.2
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• pp.73-85
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• 2014

This study analyzed the profile of a Facebook account to examine how personal information is revealed and what kinds of factors influence personal information revelation. Categories of user's profile on Facebook were analyzed and two dimensions were developed; the degree that how much personal information is revealed and the network limits that personal information is accessed. Main variables to determine personal information revelation are Facebook privacy concern and uses for social relationships along with gender, the duration of Facebook use, and average time of use. Data were collected from college students. Factor analysis produced two factors of Facebook privacy concern, Facebook privacy concern with users and Facebook privacy concern with the Facebook system. Regression analyses were performed to identify significant determinants of the degree of information revelation and the network limits of personal information. The results found out that the degree of personal information revelation is explained by gender, the duration of use, and use for social relationships while the network limit is explained by the duration of use and Facebook privacy concern with users. Worthy of notice is that use for social relationships and Facebook privacy concern with the Facebook system offset each other. The implications of the results are discussed. Additionally and finally the categories of profiles are graphically re-grouped to show how personal information revelation is associated with social relationship generation and maintenance.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.71-86
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• 2023

The purpose of this exploratory study is to compare the differences in public perceptual characteristics of Korean and Chinese societies regarding the metaverse using big data analysis. Due to the environmental impact of the COVID-19 pandemic, technological progress, and the expansion of new consumer bases such as generation Z and Alpha, the world's interest in the metaverse is drawing attention, and related academic studies have been also in full swing from 2021. In particular, Korea and China have emerged as major leading countries in the metaverse industry. It is a timely research question to discover the difference in social awareness using big data accumulated in both countries at a time when the amount of mentions on the metaverse has skyrocketed. The analysis technique identifies the importance of key words by analyzing word frequency, N-gram, and TF-IDF of clean data through text mining analysis, and analyzes the density and centrality of semantic networks to determine the strength of connection between words and their semantic relevance. Python 3.9 Anaconda data science platform 3 and Textom 6 versions were used, and UCINET 6.759 analysis and visualization were performed for semantic network analysis and structural CONCOR analysis. As a result, four blocks, each of which are similar word groups, were driven. These blocks represent different perspectives that reflect the types of social perceptions of the metaverse in both countries. Studies on the metaverse are increasing, but studies on comparative research approaches between countries from a cross-cultural aspect have not yet been conducted. At this point, as a preceding study, this study will be able to provide theoretical grounds and meaningful insights to future studies.