• Journal of Internet Computing and Services
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• v.11 no.6
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• pp.135-144
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• 2010

Apple recently announced a new policy about software development kit that banned the use of tools that convert apps built on other platforms into iPhone apps. Therefore, Adobe cannot develop their software to AppStore that inquire to the Department of Justice and the Federal Trade Commission about antitrust actions. Someone argue that Apple try to exclusive smartphone market such as the Microsoft antitrust lawsuit in 1998, but this case is essentially different. First, it need to define Apple's software development kit for iPhone and iPad is whether antitrust or not. Because of the characteristics of two-sided market in Smartphone Apple's iPhone cannot monopoly in cellphone or smartphone market, but it can be an antitrust in application store market. However, Apple re-announced new software development kit policy that shows positive results. Instead of hastily intervened regulatory agencies, the DOJ or the FTC, it is quite desirable that watching the interaction between companies that whether market failures or not and if it's harmful for consumer's benefit. Adobe attack Apple to advocate consumers and developers freedom of choice, but the most important thing is conclusion based on a comprehensive analysis need to objective point of view that Apple do whether antitrust act or not and damage to developers and consumers who are both side of platform.

• Journal of Information Technology Services
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• v.15 no.3
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• pp.195-209
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• 2016

IoT (Internet of Things) is a collective term referring to application services that provide information through sensors/devices connected to the internet. The real world application of IoT is expanding fast along with growing number of sensors/devices. However, since IoT application relies on vertical combination of sensors/devices networks, information sharing within IoT services remains unresolved challenge. Consequently, IoT sensors/devices demand high construction and maintenance costs, rendering the creation of new IoT services potentially expensive. One solution is to launch an IoT open market for information sharing similar to that of App Store for smart-phones. Doing so will efficiently allow novel IoT services to emerge across various industries, because developers can purchase licenses to access IoT resources directly via an open market. Sharing IoT resource information through an open market will create an echo-system conducive for easy utilization of resources and communication between IoT service providers, resource owners, and developers. This paper proposes the new business model of IoT open market for information sharing, and the requirements for ensuring security and standardization of open markets.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.16 no.3
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• pp.23-32
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• 2012

Objectives: The objective of this study is to investigate and valuate currently provided database management systems (DBMS) of traditional Korean medical knowledge. Methods: We searched DBMS on the web and smart device application markets (Apple App Store and Google Play Store). Key words for searching were 'traditional medicine', 'acupuncture', 'moxibustion', 'herbal medicine', and '한의학'. We looked into each DBMS to find out its scopes and limits, and each was valuated according to its functionality, accessibility, and utility. Results: 186 DBMS of traditional Korean medical knowledge were investigated and 91% of them were applications for smart devices. Almost all DBMS provided acupuncture and herb information, and a small amount of DMBS provided prescription and research paper information. Functionality, accessibility, and utility valuation were performed by using scoring system from 0 to 2. Mean values of functionality, accessibility, and utility were 0.86, 1.29, and 1.09. Conclusions: On the whole, high accessibility and low functionality were found, and various data-calculating functions were not implemented. Further researches and developments about traditional Korean medical knowledge DBMS are necessary to provide correct traditional Korean medical information and to support the studies about Korean medicine.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.1
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• pp.195-203
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• 2019

Mobile applications stores such as Google Play Store and Apple App Store, are widely used to distribute a variety of applications including finance, shopping, and entertainment. Recently, however, vulnerabilities of the mobile applications are likely to violate users' privacy such as personal information leakage. In this paper, we classify mobile applications that can be download from mobile stores, and analyze the personal information that could be leaked when users are using the mobile applications. As a result of analysis, we found that personal information are leaked in some widely used mobile applications in practice. On the basis of our experiment results, we propose some mitigations to enhance security of the mobile applications and prevent leakage of personal information.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.98-101
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• 2022

Before the delivery platform was established, when consumers ordered by phone to the store, a rider hired by the store directly delivered it or delivered it through a delivery agency. However, as the food service culture develops, delivery platforms grow and more people seek convenience, making it difficult to see orders without delivery platforms now. When ordering using the delivery platform in this way, there is a shortage of riders and a negative effect of increasing delivery costs for supply. Therefore, we propose a joint delivery chat app to solve the problems of the current delivery system and reduce delivery costs.

• Smart Media Journal
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• v.12 no.11
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• pp.27-35
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• 2023

Fourth Industrial Revolution and COVID-19 pandemic have boosted the use of Government 24 app for public service complaints in the era of non-face-to-face interactions. there has been a growing influx of complaints and improvement demands from users of public apps. Furthermore, systematic management of public apps is deemed necessary. The aim of this study is to analyze the grievances of Government 24 app users, understand the current dissatisfaction among citizens, and propose potential improvements. Data were collected from the Google Play Store from May 2, 2013, to June 30, 2023, comprising a total of 6,344 records. Among these, 1,199 records with a rating of 1 and at least one 'thumbs-up' were used for topic modeling analysis. The analysis revealed seven topics: 'Issues with certificate issuance,' 'Website functionality and UI problems,' 'User ID-related issues,' 'Update problems,' 'Government employee app management issues,' 'Budget wastage concerns ((It's not worth even a single star) or (It's a waste of taxpayers' money)),' and 'Password-related problems.' Furthermore, the overall trend of these topics showed an increase until 2021, a slight decrease in 2022, but a resurgence in 2023, underscoring the urgency of updates and management. We hope that the results of this study will contribute to the development and management of public apps that satisfy citizens in the future.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.1-18
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• 2015

Collaborative filtering(CF) algorithm has been popularly used for recommender systems in both academic and practical applications. A general CF system compares users based on how similar they are, and creates recommendation results with the items favored by other people with similar tastes. Thus, it is very important for CF to measure the similarities between users because the recommendation quality depends on it. In most cases, users' explicit numeric ratings of items(i.e. quantitative information) have only been used to calculate the similarities between users in CF. However, several studies indicated that qualitative information such as user's reviews on the items may contribute to measure these similarities more accurately. Considering that a lot of people are likely to share their honest opinion on the items they purchased recently due to the advent of the Web 2.0, user's reviews can be regarded as the informative source for identifying user's preference with accuracy. Under this background, this study proposes a new hybrid recommender system that combines with users' review mining. Our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and his/her text reviews on the items when calculating similarities between users. In specific, our system creates not only user-item rating matrix, but also user-item review term matrix. Then, it calculates rating similarity and review similarity from each matrix, and calculates the final user-to-user similarity based on these two similarities(i.e. rating and review similarities). As the methods for calculating review similarity between users, we proposed two alternatives - one is to use the frequency of the commonly used terms, and the other one is to use the sum of the importance weights of the commonly used terms in users' review. In the case of the importance weights of terms, we proposed the use of average TF-IDF(Term Frequency - Inverse Document Frequency) weights. To validate the applicability of the proposed system, we applied it to the implementation of a recommender system for smartphone applications (hereafter, app). At present, over a million apps are offered in each app stores operated by Google and Apple. Due to this information overload, users have difficulty in selecting proper apps that they really want. Furthermore, app store operators like Google and Apple have cumulated huge amount of users' reviews on apps until now. Thus, we chose smartphone app stores as the application domain of our system. In order to collect the experimental data set, we built and operated a Web-based data collection system for about two weeks. As a result, we could obtain 1,246 valid responses(ratings and reviews) from 78 users. The experimental system was implemented using Microsoft Visual Basic for Applications(VBA) and SAS Text Miner. And, to avoid distortion due to human intervention, we did not adopt any refining works by human during the user's review mining process. To examine the effectiveness of the proposed system, we compared its performance to the performance of conventional CF system. The performances of recommender systems were evaluated by using average MAE(mean absolute error). The experimental results showed that our proposed system(MAE = 0.7867 ~ 0.7881) slightly outperformed a conventional CF system(MAE = 0.7939). Also, they showed that the calculation of review similarity between users based on the TF-IDF weights(MAE = 0.7867) leaded to better recommendation accuracy than the calculation based on the frequency of the commonly used terms in reviews(MAE = 0.7881). The results from paired samples t-test presented that our proposed system with review similarity calculation using the frequency of the commonly used terms outperformed conventional CF system with 10% statistical significance level. Our study sheds a light on the application of users' review information for facilitating electronic commerce by recommending proper items to users.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.709-722
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• 2014

In the $21^{st}$ century, IT reform has led to the development of cyber-infrastructure owing to the outstanding enhancement of computer and network performance. The ripple effect has continued to increase. Accordingly, this study suggests a new computational research environment using mobile devices. In order to simplify the access of supercomputer, Science AppStore, task management and virtualization technologies are developed on mobile devices. User can be able to research by utilizing computational science SW such as compressible flow solver and nano device simulation tool that in installed on supercomputer in mobile environments. Also, this research environment makes it possible to monitor the simulation result and covers 14 university, 33 subjects, and 1,202 individuals.

• 한국어정보학회:학술대회논문집
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• 2016.10a
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• pp.291-295
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• 2016

본 논문에서는 한국어 기본 유니트 단위로 WPM을 활용한 구글 플레이 스토어 앱의 댓글 감정분석을 수행하였다. 먼저 자동 띄어쓰기 시스템을 적용한 후, 어절단위, 형태소 분석기, WPM을 각각 적용하여 모델을 생성하고, 로지스틱 회귀(Logistic Regression), 소프트맥스 회귀(Softmax Regression), 서포트 벡터머신(Support Vector Machine, SVM)등의 알고리즘을 이용하여 댓글 감정(긍정과 부정)을 비교 분석하였다. 그 결과 어절단위, 형태소 분석기보다 WPM이 최대 25%의 향상된 결과를 얻었다. 또한 분류 과정에서 로지스틱회귀, 소프트맥스 회귀보다는 SVM 성능이 우수했으며, SVM의 기본 파라미터({'kernel':('linear'), 'c':[4]})보다 최적의 파라미터를 적용({'kernel': ('linear','rbf', 'sigmoid', 'poly'), 'C':[0.01, 0.1, 1.4.5]} 하였을 때, 최대 91%의 성능이 나타났다.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.9 no.4
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• pp.131-140
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• 2010

Recently, mobile wireless data traffic has been dramatically increased due to not only the popularization of digital convergence devices including smart phone, Net-book, and Tablet PC, but also the vitalization of wireless Internet related eco-systems such as AppStore. In addition, it is expected that a tremendous increase in mobile data is caused by the release of unlimited mobile data plans (flat-fee). In order to deal with such mobile data traffic explosion, it is necessary that network operators should make efforts to offload wireless data traffic. This paper reviews the condition of mobile wireless data traffic in domestic and international telecommunication industry and looks for various action plans to overcome the difficulty of network operators.