• Architectural research
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• v.18 no.1
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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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• v.34 no.1
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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.

• Journal of Information Technology Services
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• v.14 no.3
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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.

• Korean Journal of Ecology and Environment
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• v.34 no.3 s.95
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• pp.141-152
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• 2001

Abundance of autotrophic picoplankton (APP) and their contribution to phytoplankton biomass were assessed in seven brackish lagoons and five freshwater reservoirs in the summer season. Phycocyanin-rich picocyanobacteria dominated APP in lagoons, while phycoerythrin-rich picocyanobacteria dominated APP in freshwater reservoirs. The cell density of APP ranged from $3.6{\times}10^3$ to $5.0{\times}10^6\;cells/ml$ (median $2.5{\times}10^5$) in brackish lagoons and from $3.8{\times}10^4$ to $3.6{\times}10^5\;cells/ml$ (mdian $1.3{\times}10^5$) in reservoirs. Carbon biomass ranged from 1.0 to $1,385.0\;{\mu}gC/L$ in lagoons and from 15.3 to $128.2\;{\mu}gC/L$ in reservoirs. APP cell density in Lake Kyungpo was over $10^6\;cells/ml$in all three surveys, which is one of the highest values recorded in all over the world. During the thermal stratification in Lake Soyang, the maximum abundance of APP and their maximum contribution to phytoplankton biomass were observed near the thermocline. This study showed that APP sometimes can contribute significantly to phytoplankton biomass both in lagoons and reservoirs with the range from 0.1 to 85.0%. APP which have been overlooked in the past studies appears to be important primary producers in Korean lake ecosystem.

• Journal of Information Technology Services
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• v.18 no.1
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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.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.55-66
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• 2021

The mobile application (APP) market is growing at an unprecedented speed. Amid such growth, the global platform providers are mandating exclusive in-app payments and charging 30% for platform commission fees. A serious tension has arisen between mobile global platform providers and local content providers. The present study attempts to analyze the domestic mobile game market and in-app payment commission fees. This study estimates the size of the domestic mobile game market and platform commission fees by directly using publicly available financial statements and footnote information of some representative listed mobile game firms. Also, the study analyzes the cost structures of the same sample firms and attempts to draw some implications on sustainable growths of the mobile game ecosystem. We estimated that, in 2019, the domestic mobile game market is around 4.9 trillion Won and the ensuing in-app payment commission fees market was 1.5 trillion Won. High market share firms display a proportional increase in in-app payment commission fees in relation to sales growth. This, in turn, makes the in-app payment commission fees a primary cost item far exceeding employee salaries and R&D expenses. During the same period, low market share firms generated a mere profit or experienced net loss. Analysis of the cost structure reveals that these firms are even more liable to higher in-app payment commission fee cost structure than high market share. Most constituents of the mobile game ecosystem are small business entrepreneurs. By employing a micro-level analysis, the study estimates that, in 2019, a representative median firm generates 530 million Won in sales. At the same time, it spends 190 million Won in employee salaries, 50 Won million in R&D and 190 million Won in in-app payment commission fees, respectively. In the absence of other cost items, these three cost items alone account for 73.8% of sales revenue. The results imply that a sustainable growth of the local mobile game market heavily depends upon the cost structure of such representative median firm, the in-app payment commission fees being the primary cost item of such firm.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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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.

• Journal of Digital Convergence
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• v.13 no.6
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• pp.1-12
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• 2015

In recent years, the privacy concern for mobile consumers is emerging as the use of mobile application(apps) is growing according to the rapid spread of mobile devices such as smart phones and tablet PCs. To improve privacy protections in the mobile communications and apps, overseas organizations are announcing guidelines and/or checklists for stake holders. Although personal information protection guidelines for application developers have been prepared in the country, efforts to improve consumer privacy capability is insufficient. Thus, in this paper we first scope the app privacy related guidelines in both domestic and foreign affairs, then present the risk factors of privacy invasion by the stage of mobile application use based on the "Privacy Protection Act", offering privacy checklists for consumers. This checklist will enhance the self-management capability of consumer privacy and create virtuous cycle in the mobile ecosystem.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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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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• v.14 no.2
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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.