• Journal of Information Technology Applications and Management
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• v.20 no.2
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• pp.57-68
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• 2013

Recently, cloud computing has been viewed as a technology of great influence. Despite the importance of personal cloud services, the majority of cloud computing studies has been focused on an enterprise level. Therefore, in this research, we list variables of personal cloud computing characteristics (accessibility, storage, trust, perceived ease of use, availability) from preceding research done on an enterprise level. So, through pilot testing, we analyze whether examined characteristic variables affect the intention to use personal cloud computing. Next, we investigate the relevancy between user characteristics, including gender, age, and occupation, and the usage rate of personal cloud computing. Lastly, we implement an analysis of variance in order to speculate whether there is any difference between the five characteristic variables in using personal cloud computing between cloud service enterprises. For empirical testing people with experience in personal cloud computing are surveyed. Frequency analysis, exploratory factor analysis, internal consistency analysis, T-test, ANOVA, and multiple regression analysis are implemented with 325 valid samples. The analysis results are as follows: First, among user characteristic variables, 'occupation' shows a significant relationship with cloud service usage, Second, between cloud service enterprises, all five characteristic variables of 'Naver N Drive' are thoroughly evaluated and 'Google Drive' receives a high evaluation in availability and accessibility. Third and finally, all of the selected characteristic variables affect the intention to use personal cloud computing. However, availability is identified to be the most important factor. This research will serve as a reference to enterprises for the practical implementation of personal cloud computing service.

• Journal of Information Technology and Architecture
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• v.10 no.3
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• pp.371-378
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• 2013

Video-demand learning through E-learning continuously increases on these days. However, not all video-demand learning systems can be utilized properly. When students study by education videos not matched to level of their own, it is possible for them to lose interest in learning. It causes to reduce the learning efficiency. In order to solve the problem, we need to develop a recommendation system which recommends customized education videos according the study levels of students. In this paper, we estimate the study level based on the history of students' watching behaviors such as average watching time, skipping and rewinding of videos. In the experimental section, we demonstrate our recommendation system using real students' video watching history to show that our system is feasible in a practical environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.285-287
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• 2015

Recently, the development of sensors and the mobile communication device offers a number of possibilities in the medical and related fields. However, this data is generated, it is difficult to match the metadata and standard units. The data integration is required to use the data generated by the different specifications of the sensor efficiently. Accordingly, in this paper we propose a method using an ontology as a method to integrate the data generated by the existing sensors and the new sensor. The ontology is mapping to the standard item and sensors, also include a type and structural difference. The mapping is comprised of two : data mapping, and metadata mapping. There are standard items that are created in this way, type of data exchange between services. This can solve the heterogeneous problem generated by sensors.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.286-289
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• 2016

In this paper, we propose an architecture that affords mobile app based on nomadic smartphone using not only mobile cloud computing- architecture but also a dedicated web platform called Node.js built-in with the asynchronous, Nonblocking, Event-Driven programming paradigm. Furthermore, the design of the proposed architecture takes document oriented database known as MongoDB to deal with the large amount of data transmit by users of mobile web access application. The Node.js aims to give the programmers the tools needed to solves the large number of concurrent connections problem. We demonstrate the effectiveness of the proposed architecture by implementing an android application responsible of real time analysis by using a vehicle to applications smart phones interface approach that considers the smartphones to acts as a remote users which passes driver inputs and delivers output from external applications.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.129-136
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• 2015

We investigate the underlying structure of big data research domain, which is diversified and complicated using bottom-up approach. For that purpose, we derive a set of articles by searching "big data" through the Korea Citation Index System provided by National Research Foundation of Korea. With some preprocessing on the author-provided keywords, we analyze bibliometric data such as author-provided keywords, publication year, author, and journal characteristics. From the analysis, we both identify major sub-domains of big data research area and discover the hidden issues which made big data complex. Major keywords identified include SOCIAL NETWORK ANALYSIS, HADOOP, MAPREDUCE, PERSONAL INFORMATION POLICY/PROTECTION/PRIVATE INFORMATION, CLOUD COMPUTING, VISUALIZATION, and DATA MINING. We finally suggest missing research themes to make big data a sustainable management innovation and convergence medium.

• Journal of Convergence for Information Technology
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• v.7 no.5
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• pp.177-182
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• 2017

As major ICT technologies such as IoT, cloud computing, and Big Data are being applied to manufacturing, smart factories are beginning to be built. The key of smart factory implementation is the ability to acquire and analyze data of the factory. Therefore, the need for a big data analysis platform is increasing. The purpose of this study is to construct a platform for big data analysis of manufacturing process and propose integrated method for analysis. The proposed platform is a RHadoop-based structure that integrates analysis tool R and Hadoop to distribute a large amount of datasets. It can store and analyze big data collected in the unit process and factory in the automation system directly in HBase, and it has overcome the limitations of RDB - based analysis. Such a platform should be developed in consideration of the unit process suitability for smart factories, and it is expected to be a guide to building IoT platforms for SMEs that intend to introduce smart factories into the manufacturing process.

• Journal of Digital Convergence
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• v.14 no.7
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• pp.373-383
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• 2016

With mobile-epoch and emerging of Fin-tech, Bio-recognition technology utilizing bio-information in secure method has spread. Specially, In order to change convenient payment services and transportation cards, the combination of biometrics and mobile services are being expanded. The basic concept of authentication such as access control, IA&A, OpenID, OAuth 1.0a, SSO, and Biometrics techniques are investigated, and the protocol stack for security API platform, FIDO, SCIM, OAuth 2.0, JSON Identity Suite, Keystone of OpenStack, Cloud-based SSO, and AIM Agent are described detailed in aspect of application of AIM. The authentication technology in domestic and foreign will accelerate technology development and research of standardization centered in the federated FIDO Universal Authentication Framework(UAF) and Universal 2 Factor Framework(U2F). To accommodate the changing needs of the social computing paradigm recently in this paper, the trends of various authentication technology, and design and function of AIM framework was defined.

• Informatization Policy
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• v.21 no.2
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• pp.24-48
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• 2014

Smart working, along with the spread of smart device use, has recently garnered increased interest. The issue has become focused on low labor productivity over working time, work-life balance and social discussion. There has also been a shift in working styles due to the development of mobile, security, and cloud computing-related IT technology support and activation of the smart work environment. However, former research show concern that smart working may not meet the initially expected levels of productivity. This empirical study was carried out to introduce the impact of teleworking on increased productivity and satisfaction in companies and public institutions through user interviews, a socio-technical systems approach and surveys. To summarize the results of this study, 'Telework satisfaction' is directly influenced by 'Work-life balance', 'Telework-system fit, and 'Telework-job fit'. Direct impact factors of 'Telework productivity'are 'Telework-job fit', 'Telework satisfaction', and 'Telework environment quality'.

• Journal of Information Technology Services
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• v.14 no.3
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• pp.99-116
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• 2015

The concept of Cloud computing has been introduced in the IT field over 10 years and industry has been expanding constantly. However, compare to the maturity of global market, Korea cloud computing industry is only in the early stage. Even the Korea has advantages in technology infrastructure; the pace of Korea cloud computing market growth is taking a serious downturn. Under these circumstances, it is needed to be discussing that strategy for expanding the cloud computing market size and for sustaining global competitiveness of local companies. Previous studies on plans for Korea cloud computing market has been conducted since 2009 and most of them are tend to examined in demand perspective. Thus, this study aims at identifying the priority of business challenges for making better performance in the market with service provider aspects. To analyze the important factors in the providing cloud computing service, ANP methodology was applied in this study. The network model including five clusters, security, stability, performance, consumer, and institution, was defined through literature review and expert survey was conducted to collect data. As a result of ANP analysis, 'Securing service reliability' was analyzed as the most important factor and followed by 'Preparing the range of legal liability', 'Preventing personal information leakage' and 'Preventing confidential information data leakage.' The priority of result indicates that service provider needs to focus on to make the secured service environment. This study has significance on analyzing the priority of business challenges in the service provider perspective. This study will provide useful guidelines to for establishing strategies in cloud computing market.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.503-509
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• 2019

Order-revealing encryption which enables a range query over encrypted data is attracting attention as one of the important security technologies in industry such as IoT, smart manufacturing, and cloud computing. In 2015, an ideally-secure order-revealing encryption whose ciphertexts reveal no additional information beyond the order of the underlying plaintexts has been proposed. However, their construction is too inefficient for practical use and some security analysis of multilinear maps, which their construction relies on, have been proposed. Recently, more practical schemes have been proposed, focusing on achieving practically usable efficiency rather than the ideal security. In this paper, we propose a more storage-efficient order-revealing encryption scheme than the Lewi et al.'s scheme most recently published by presenting an idea that can generate shorter ciphertexts without any security loss.