• Journal of Information Technology Services
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• v.21 no.5
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• pp.15-28
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• 2022

Recently, government agencies are actively adopting the platform model as a means of public policy. However, existing studies on the public platform are minimal and have focused on user experiences or the possibility of public usage of the platform model. Now the research concerning building governance structure and utilizing network effects of the platform after adopting the platform model in the public sector is keenly required. This study intended to ignite academic dialogue on the governance of public platforms in the context of digital transformation. This study focused on a case of the 'Special delivery,' a public delivery app established by Gyeonggi-do. In order to analyze the characteristics of the public platform and its governance structure, data were collected from press releases, policy reports, and news articles. Data was analyzed using the frame of Hagui's platform design factors and Ansell & Gash's collaborative governance model. The results of the public platform analyses showed 1) incompleteness in the value trade-off accounting, which was designed for platform business based on general cost-benefit analysis, and 2) a closed governance structure that limits direct participation of diverse user groups(i.e., service provider, customer) in order to enhance providers' utility by preventing customers' excessive online activities. The results of this study provided theoretical and policy implications regarding designing the strategy for accounting for value trade-offs and functioning governance structure for public platforms.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.123-130
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• 2018

With the emergence of a new paradigm called Open Science and Big Data, the need for data sharing and collaboration is also emerging in the computational science field. This paper, we analyzed data-driven research cases for computational science by field; material design, bioinformatics, high energy physics. We also studied the characteristics of the computational science data and the data management issues. To manage computational science data effectively it is required to have data quality management, increased data reliability, flexibility to support a variety of data types, and tools for analysis and linkage to the computing infrastructure. In addition, we analyzed trends of platform technology for efficient sharing and management of computational science data. The main contribution of this paper is to review the various computational science data repositories and related platform technologies to analyze the characteristics of computational science data and the problems of data management, and to present design considerations for building a future computational science data platform.

• Smart Media Journal
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• v.9 no.2
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• pp.99-109
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• 2020

This paper proposes a platform construction plan for the use of spectrum big data, collects and analyzes the big data in the radio wave field, establishes a linkage plan, and presents a support system scheme for linking and using the spectrum and public sector big data. It presented a plan to build a big data platform in connection with the spectrum public sector. In a situation where there is a lack of a support system for systematic analysis and utilization of big data in the field of radio waves, by establishing a platform construction plan for the use of big data by radio-related industries, the preemptive response to realize the 4th Industrial Revolution and the status and state of the domestic radio field. The company intends to contribute to enhancing the convenience of users of the big data platform in the public sector by securing the innovation growth engine of the company and contributing to the fair competition of the radio wave industry and the improvement of service quality. In addition, it intends to contribute to raising the social awareness of the value of spectrum management data utilization and establishing a collaboration system that uses spectrum big data through joint use of the platform.

• The Journal of Bigdata
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• v.2 no.2
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• pp.9-16
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• 2017

Global Platform Solution Company (aka Amazon, Google, MS, IBM) who has cloud platform, are driving AI and Big Data service on their cloud platform. It will dramatically change Enterprise business value chain and infrastructures in Supply Chain Management, Enterprise Resource Planning in Customer relationship Management. Enterprise are focusing the channel with customers and Business Partners and also changing their infrastructures to platform by integrating data. It will be Digital Transformation for decision support. AI and Deep learning technology are rapidly combined to their data driven platform, which supports mobile, social and big data. The collaboration of platform service with business partner and the customer will generate new ecosystem market and it will be the new way of enterprise revolution as a part of the 4th industrial revolution.

• Journal of Engineering Education Research
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• v.22 no.5
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• pp.37-48
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• 2019

The government has tried to develop a platform for systematically collecting and managing engineering education data for policy proposals. However, there have been few cases of big data analysis platform for policy proposals in engineering education, and it is difficult to determine the major function of the platform, the purpose of using big data, and the method of data collection. This study aims to collect the cases of big data analysis systems for the development of a big data system for educational policy proposals, and to conduct a study to analyze cases using the analysis frame of key elements to consider in developing a big data analysis platform. In order to analyze the case of big data system for engineering education policy proposals, 24 systems collecting and managing big data were selected. The analysis framework was developed based on literature reviews and the results of the case analysis were presented. The results of this study are expected to provide from macro-level such as what functions the platform should perform in developing a big data system and how to collect data, what analysis techniques should be adopted, and how to visualize the data analysis results.

• Korean Journal of Computational Design and Engineering
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• v.19 no.4
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• pp.434-442
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• 2014

Most manufacturing enterprises create CAD data as a result of the product/part design process; however, the CAD data is being utilized only for production activities. Besides the processes directly related to manufacturing such as design and production, the CAD data is an important resource that can be used in variety of services (e.g., catalog production and production manuals) for manufacturing enterprises. This study proposes a software platform that can support a wide range of services for manufacturing companies in an efficient and productive way. The software platform was designed based on the functions identified by requirement analysis. The platform consists of four layers: data model layer to manage relevant data; library layer and common function layer to configure services; and application layer to install and run the software. Finally, this study evaluates the validity of the proposed platform architecture by applying it to the digital catalog system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.1-15
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• 2019

Internet of Things (IoT) is commonly referred to as a future internet technology to provide advanced services by interconnecting physical and virtual things, collecting and using many data from them. The IoT platform is a server platform with a common architecture to collect and share the data independent of the IoT devices and services. Recently, oneM2M, the global standards initiative for Machine-to-Machine (M2M) communications and the IoT announced the availability of oneM2M Release 2 specifications. Accordingly, this paper presents a new oneM2M-compliant IoT platform called Mobius 2.0 and proposes its application to collect the biosignal data from wearable IoT devices for emotion recognition. Experimental results show that we can collect various biosignal data seamlessly and extract meaningful features from the biosignal data to recognize two emotions of joy and sadness.

• Journal of Platform Technology
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• v.9 no.1
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• pp.23-31
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• 2021

In recent years, in the domestic industry, personal damage has occurred due to sensor malfunction and the emission of harmful gases. But there is a limit to the reliability verification of sensor data because the evaluation of environmental sensors is focused on durability and risk tests. This platform designed a sensor board that measures 10 major substances and a performance verification system for each sensor. In addition, the data collected by the sensor board was transferred to the server for data reliability evaluation and verification using LoRa communication, and a prototype of the sensor data platform was produced to monitor the transferred data. And the collected data is analyzed and predicted by using machine learning techniques.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.3
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• pp.325-350
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• 2023

This study focuses on the impact of research data metadata quality evaluation index on the revitalization of K-BDS, a research data platform in the bio field, and examines the mediating effect of motivation factors for utilizing the platform. The investigation employs a structural equation model analysis and bootstrap analysis to explore the interrelationships among the three variables. The findings demonstrate that researchers who prioritize the quality of metadata display higher motivation to use the research data platform, leading to an intention to activate the platform. The study also confirms the mediating effect of motivation factors. Moreover, a comprehensive understanding of the sub-factors within each variable is attained through regression analysis and Sobel test. The results highlight that enhancing searchability is crucial to activate research data sharing in the bio field, while improving discoverability is vital for research data reuse. Interestingly, the study reveals that citationability does not significantly impact platform activation. As a conclusion, to foster platform activation, it is imperative to provide systematic support by enhancing metadata quality. This improvement can not only increase trust in the platform but also institutionally solidify the benefits of citation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.579-580
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• 2021

It is representative that utilize the platform embedded by each sensor for gathering data from sensor. However, various platforms have different implementations for different types of sensors, making it difficult to apply them at once. Therefore, in this paper, we propose a web-based data collection platform that can easily gathering and managing various sensing data on one platform.