• Architectural research
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• 제18권1호
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• pp.13-20
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• 2016

Smart Home has achieved remarkable developments over the past few decades. In the ICT(Information and Communications Technology) field, 'app ecosystem'-a collection of multiple devices such as mobile phones and tablets, software (operating system and development tools), companies (manufacturers, carriers, app-stores, etc.) and the process through which data is transferred/shared by a user from one device to another device or by the device itself-has come into wide use since the advent of the smart phone. Due to the synergy effect of the 'app ecosystem', it has been applied to various fields such as televisions and automobile industries. As a result, both the Smart TV and connected vehicle have developed their own ecosystem. Although much research has been conducted on these two ecosystems, there is a lack of research regarding 'App Ecosystem based Smart Home' (AESH). This research focuses on the building scenarios based on 'Tracking, Analyzing, Imaging, Deciding, and Acting (T.A.I.D.A), a future prediction method process. Rather than taking an approach from the perspective of providing and applying advanced technology for research on building future scenarios, this paper focuses on research from the perspective of architectural planning. As a result, two future scenarios of AESH are suggested.

• Asia pacific journal of information systems
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• 제34권1호
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• pp.150-190
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• 2024

This article proposes a framework to elucidate the structural dynamics of the generative AI ecosystem. It also outlines the practical application of this proposed framework through illustrative policies, with a specific emphasis on the development of the Korean generative AI ecosystem and its implications of platform strategies at AI platform-squared. We propose a comprehensive classification scheme within generative AI ecosystems, including app builders, technology partners, app stores, foundational AI models operating as operating systems, cloud services, and chip manufacturers. The market competitiveness for both app builders and technology partners will be highly contingent on their ability to effectively navigate the customer decision journey (CDJ) while offering localized services that fill the gaps left by foundational models. The strategically important platform of platforms in the generative AI ecosystem (i.e., AI platform-squared) is constituted by app stores, foundational AIs as operating systems, and cloud services. A few companies, primarily in the U.S. and China, are projected to dominate this AI platform squared, and consequently, they are likely to become the primary targets of non-market strategies by diverse governments and communities. Korea still has chances in AI platform-squared, but the window of opportunities is narrowing. A cautious approach is necessary when considering potential regulations for domestic large AI models and platforms. Hastily importing foreign regulatory frameworks and non-market strategies, such as those from Europe, could overlook the essential hierarchical structure that our framework underscores. Our study suggests a clear strategic pathway for Korea to emerge as a generative AI powerhouse. As one of the few countries boasting significant companies within the foundational AI models (which need to collaborate with each other) and chip manufacturing sectors, it is vital for Korea to leverage its unique position and strategically penetrate the platform-squared segment-app stores, operating systems, and cloud services. Given the potential network effects and winner-takes-all dynamics in AI platform-squared, this endeavor is of immediate urgency. To facilitate this transition, it is recommended that the government implement promotional policies that strategically nurture these AI platform-squared, rather than restrict them through regulations and stakeholder pressures.

• 한국IT서비스학회지
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• 제14권3호
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• pp.1-18
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• 2015

Recent trends of mobile convergence has already brought about many changes in our digitally-powered society. Especially, taking advantage of strengths of existing mobile devices and smart phones have already been established as a primary standard in the business intelligence world. Such high-powered digital devices equipped with mobile convergence functions are getting more momentum as app stores are prevailing. Basically, the app stores are administered by smart phone manufacturers, creating a new business ecosystem among app developers and end-users. However, there are paucity of studies tackling an issue about how users' repurchase intention of the apps is influenced by the service qualities of the app stores. In this respect, this study aims to investigate the effect of app store service quality on users' satisfaction and repurchase intention. As the value of loyal customers is incomparably high in app commerce, winning customers' loyalty is vital to the success of app stores. In this study, a customer is defined as one who has purchased goods or services at least once from the app stores. The proposed research model includes a number of constructs such as app perceptions, customer service, perceived ease of use, design, promotion, perceived consumer risk and connectivity. Empirical results revealed that perceived consumer risk has a negative relationship with consumer's perceived repurchase intention. All the other variables-app perceptions, customer service, perceived ease of use, design, promotion, connectivity- are found to be positively related with the repurchase intentions.

• 생태와환경
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• 제34권3호통권95호
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• pp.141-152
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• 2001

1999년 여름동안 동해안의 7개 석호와, 내륙의 5개 인공호를 대상으로 autotrophic picoplankton (APP)의풍부도와 총 식물플랑크톤 생물량에 대한 기여도를 평가하였다. 석호에서는 부영양화가 심한 호수에서 주로 나타나는 phycocyanin-rich APP가 우점한 반면 인공호에서는 주로 phycoerythrin-rich APP가 우점하였다. 동해안 석호에서 APP의 세포밀도와 생물량은 각각 $3.6{\times}10^3{\sim} 5.0{\times}10^6\;cells/ml$와 $1.0{\sim}1,385.0\;{\mu}gC/L$의 범위를 나타냈고 인공호에서는 각각 $3.8{\times}10^4{\sim}3.6{\times}10^5\;cells/ml$와 $15.3{\sim}128.2\;{\mu}gC/L$의 범위로 동해안석호에서 더 높은 APP의 풍부도를 나타냈다. 특히 경포호에서는 3회조사 모두 $10^6\;cells/ml$이상의 높은 세포밀도를 보였는데 이는 세계적으로 보고된 가장 높은 밀도 수준이다. 소양호에서 APP의 수직분포를 조사한 결과 수온약층에서 최대 세포밀도를 나타냈는데 이는 APP가 낮은 광도에서도 성장이 가능하며 심층의 높은 영양염류를 이용할 수 있었기 때문으로 사료된다. APP세포밀도는TN/TP비와 음의 상관관계,TP와는 양의 상관관계를 나타내어 APP의 풍부도가 호수의 부영양화와 함께 증가하는 경향을 나타내었다. 총 식물플랑크톤 생물량에 대한 APP의 기여도는 $0.1{\sim}85.0%$로 호수간의 큰 차이를 보였다. 본 연구결과는 지금까지 식물플랑크톤 연구에서 소외되어져 왔던 APP가 호수 생태계의 일차생산자로써 중요한 부분을 차지하고 있음을 보여주고 있다.

• 한국IT서비스학회지
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• 제18권1호
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• pp.201-217
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• 2019

Cars have become a necessity in modern life. It is widely used to transport people or products to a destination conveniently. However, the addition of a navigation service that provides route information and more makes driving more convenient and safer. Recent developments in the mobile app ecosystem encourages people to adopt not only an installation-type car navigation, but also a mobile app navigation, supporting connected car concepts. It should be noted that mobile apps with mobile Internet can be a significant linkage between information acquired online and offline business. This study demonstrates the impact of the app use experience for a driver in the context of applying artificial intelligence service. As a result, the introduction of artificial intelligence services has a statistically significant moderating effect on the use of mobile navigation apps. This seminal research is valuable as it evaluates the role of artificial intelligence applied to mobile navigation apps.

• 한국전자거래학회지
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• 제26권3호
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• pp.55-66
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• 2021

전 세계적으로 모바일 애플리케이션 (APP) 시장의 급성장 속에서 글로벌 플랫폼 사업자들은 30%의 플랫폼 결제수수료뿐만 아니라 인앱 결제를 강제하면서 콘텐츠 제공업체들과 갈등이 커지고 있는 상황이다. 본 연구의 목적은 콘텐츠 산업 창업 분야에서 중요한 비중을 차지하고 있는 국내 모바일 게임 시장 및 인앱 결제 수수료의 적정성을 분석하고자 한다. 본 논문에서는 상장 게임회사의 재무제표 공시자료를 직접 이용하여 국내 모바일 게임시장 및 이에 따른 플랫폼 수수료 시장 규모를 추정하였으며, 관련 게임업체들의 공시 매출 및 원가정보를 이용하여 인앱 결제 수수료가 원가 구조에 미치는 영향을 분석하고, 이것이 모바일 게임 시장의 동태적 효율성이나 생태계의 지속가능성에 미치는 함의를 도출하였다. 분석 결과, 2019년 국내 모바일 게임 시장 규모는 49,230억 원이며, 인앱 결제 수수료 규모는 14,761억 원이다. 시장점유율 상위 모바일 게임회사의 경우, 매출 중 모바일 게임 매출 비중이 높을수록 영업비용 중 인앱 결제 수수료가 차지하는 비중이 높아지며, 종업원 급여나 연구개발비와 같은 필수 비용 요소를 크게 상회하는 수준으로 지출된다. 하위 모바일 게임회사 상당수의 영업이익은 매출 대비 미미한 수준이거나 적자인 것을 알 수 있는데, 이는 인앱 결제 수수료가 매우 큰 부분을 차지하기 때문이다. 모바일 게임 생태계를 구성하는 기업들은 대부분 중소게임업체들이다. 이들의 평균(중앙치)에 해당하는 가상 기업은 2019년 기준으로 매출 5.3억 원을 올리고 있으며, 종업원 4.3명에 대한 종업원 급여 1.9억 원, 연구개발비 0.5억 원, 인앱 결제 수수료 1.6억 원을 비용으로 지출하고 있는 것으로 추정된다. 다른 비용 항목을 고려하지 않더라도 상기한 세 가지 비용만으로도 매출의 73.8%를 차지하는 원가 구조를 가지고 있으며, 인앱 결제 수수료가 모바일 생태계의 지속가능성에 큰 영향을 미치고 있음을 알 수 있다.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.141-149
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• 2023

Repacked mobile apps constitute about 78% of all malware of Android, and it greatly affects the technical ecosystem of Android. Although many methods exist for repacked app detection, most of them suffer from performance issues. In this manuscript, a novel method using the Constant Key Point Selection and Limited Binary Pattern (CKPS: LBP) Feature extraction-based Hashing is proposed for the identification of repacked android applications through the visual similarity, which is a notable feature of repacked applications. The results from the experiment prove that the proposed method can effectively detect the apps that are similar visually even that are even under the double fold content manipulations. From the experimental analysis, it proved that the proposed CKPS: LBP method has a better efficiency of detecting 1354 similar applications from a repository of 95124 applications and also the computational time was 0.91 seconds within which a user could get the decision of whether the app repacked. The overall efficiency of the proposed algorithm is 41% greater than the average of other methods, and the time complexity is found to have been reduced by 31%. The collision probability of the Hashes was 41% better than the average value of the other state of the art methods.

• 디지털융복합연구
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• 제13권6호
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• pp.1-12
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• 2015

최근 스마트폰이나 태블릿 PC 등 모바일 기기의 사용 확산과 함께 모바일 앱(어플리케이션)의 사용도 증가되면서 모바일 앱에서의 프라이버시 문제가 새롭게 대두되고 있다. 이에 해외 주요 기관에서는 관련 이해관계자들에게 가이드라인과 소비자 체크리스트를 발표하고 있다. 국내의 경우 가이드라인은 있으나, 소비자의 프라이버시 역량강화를 위한 노력이 미흡하다. 이에 본 연구에서는 국내외 모바일 앱 프라이버시 관련 가이드라인을 살펴보고, 앱 사용단계별로 소비자가 경험할 수 있는 프라이버시 위험 요인을 "개인정보보호법"에 근거하여 내용을 분석하는 것을 통해 소비자의 자율적 프라이버시 보호를 위한 체크리스트를 제시하였다. 이 체크리스트는 소비자들의 프라이버시 자율관리 역량 강화에 도움이 될 것이며, 이는 모바일 생태계의 선순환 구조를 마련하는데 일조할 것이다.

• 산업경영시스템학회지
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• 제41권1호
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• pp.128-139
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• 2018

As The Fourth Industrial Revolution and Intelligent Information Age came into full-scale, the policy of open government data has become a hot topic for each country. The United States, the United Kingdom, and other countries are shifting policy direction to "creating value" of open government data. Also, in the age of the digital economy where the data market is soaring, open government data is gradually being recognized as a new raw material for new business and start-ups. In addition, Korea ranked first in the OECD open government data evaluation twice in a row, and was highly evaluated in the international evaluation. However, domestic firms are still lacking in qualitative openness of government data, data is dispersed among institutions, lack of public-private data linkage, and development of app-oriented development. This study attempts to analyze major national policies for the creation of a data ecosystem that considers data lifecycle, from production to storage, distribution and utilization of data. First, the target countries were the leading public data countries among the OGP member countries, the USA, the UK, Australia and Canada. The results of this study are as follows. As a result of analyzing the results and comparing Korea's policies, it was concluded that most of Korea is superior in open government data policy. However, improvement of data quality, development of open data portal as an open platform, support for finding various users including apps and web development companies, and cultivation of open government data utilizing personnel are analyzed as policy issues. In addition, the direction of policy for the balanced ecosystem of Korea is presented together.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.841-854
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• 2020

The paper explains the method to process, analyze and predict traffic patterns in Los Angeles county using Big Data and Machine Learning. The dataset is used from a popular navigating platform in the USA, which tracks information on the road using connected users' devices and also collects reports shared by the users through the app. The dataset mainly consists of information about traffic jams and traffic incidents reported by users, such as road closure, hazards, accidents. The major contribution of this paper is to give a clear view of how the large-scale road traffic data can be stored and processed using the Big Data system - Hadoop and its ecosystem (Hive). In addition, analysis is explained with the help of visuals using Business Intelligence and prediction with classification machine learning model on the sampled traffic data is presented using Azure ML. The process of modeling, as well as results, are interpreted using metrics: accuracy, precision and recall.