• International conference on construction engineering and project management
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• 2022.06a
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• pp.385-392
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• 2022

As energy-saving techniques based on human behavior patterns have recently become an issue, the occupant-centered control system is adopted for estimating personal preference of indoor environment and optimizing environmental comfort and energy consumption. Accordingly, IoT devices have been used to collect indoor environmental quality (IEQ) data and personal data. However, the need to safely collect and manage data has been emerged due to cybersecurity issues. Therefore, this paper aims to present a framework that can safely transmit occupant-centered data collected from IoT to a private blockchain server using Hyperledger fabric. In the case study, the minimum value product of the mobile application and smartwatch application was developed to evaluate the usability of the proposed blockchain-based occupant-centered data collection framework. The results showed that the proposed framework could collect data safely and hassle-free in the daily life of occupants. In addition, the performance of the blockchain server was evaluated in terms of latency and throughput when ten people in a single office participated in the proposed data collection framework. Future works will further apply the proposed data collection framework to the building management system to automatically collect occupant data and be used in the HVAC system to reduce building energy consumption without security issues.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5805-5825
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• 2019

Smartphones are important platforms because of their sophisticated computation, communication, and sensing capabilities, which enable a variety of applications in the Internet of Things (IoT) systems. Moreover, advancements in hardware have enabled sensors on smartphones such as environmental and chemical sensors that make sensor data collection readily accessible for a wide range of applications. However, dynamic, opportunistic, and heterogeneous mobility patterns of smartphone users that vary throughout the day, which greatly affects the efficacy of sensor data collection. Therefore, it is necessary to consider phone users mobility patterns to design data collection schedules that can reduce the loss of sensor data. In this paper, we propose a mobility-based weighted adaptive opportunistic scheduling framework that can adaptively adjust to the dynamic, opportunistic, and heterogeneous mobility patterns of smartphone users and provide prioritized scheduling based on various application scenarios, such as velocity, region of interest, and sensor type. The performance of the proposed framework is compared with other scheduling frameworks in various heterogeneous smartphone user mobility scenarios. Simulation results show that the proposed scheduling improves the transmission rate by 8 percent and can also improve the collection of higher-priority sensor data compared with other scheduling approaches.

• Nuclear Engineering and Technology
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• v.52 no.8
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• pp.1697-1705
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• 2020

To enhance the credibility of human reliability analysis, various kinds of data have been recently collected and analyzed. Although it is obvious that the quality of data is critical, the practices or considerations for securing data quality have not been sufficiently discussed. In this work, based on the experience of the recent human reliability data extraction projects, which produced more than fifty thousand data-points, we derive a number of issues to be considered for generating meaningful data. As a result, thirteen considerations are presented here as pertaining to the four different data extraction activities: preparation, collection, analysis, and application. Although the lessons were acquired from a single kind of data collection framework, it is believed that these results will guide researchers to consider important issues in the process of extracting data.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.159-166
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• 2019

The collection and manipulation of Internet of Things (IoT) data is increasing at a fast pace and its importance is recognized in every sector of our society. For efficient utilization of IoT data, the vast and varied IoT data needs to be reliable and meaningful. In this paper, we propose an IoT framework to realize this need. The IoT framework is based on a four layer IoT architecture onto which context aware computing technology is applied. If the collected IoT data is unreliable it cannot be used for its intended purpose and the whole service using the data must be abandoned. In this paper, we include techniques to remove uncertainty in the early stage of IoT data capture and collection resulting in reliable data. Since the data coming out of the various IoT devices have different formats, it is important to convert them into a standard format before further processing, We propose the RDF format to be the standard format for all IoT data. In addition, it is not feasible to process all captured Iot data from the sensor devices. In order to decide which data to process and understand, we propose to use contexts and reasoning based on these contexts. For reasoning, we propose to use standard AI and statistical techniques. We also propose an experiment environment that can be used to develop an IoT application to realize the IoT framework.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.163-170
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• 2021

As a big data is being used in various industries, big data market is expanding from hardware to infrastructure software to service software. Especially it is expanding into a huge platform market that provides applications for holistic and intuitive visualizations such as big data meaning interpretation understandability, and analysis results. Demand for big data extraction and analysis using social media such as SNS is very active not only for companies but also for individuals. However despite such high demand for the collection and analysis of social media data for user trend analysis and marketing, there is a lack of research to address the difficulty of dynamic interlocking and the complexity of building and operating software platforms due to the heterogeneity of various social media service interfaces. In this paper, we propose a method for developing a framework to operate the process from collection to extraction and classification of social media data. The proposed framework solves the problem of heterogeneous social media data collection channels through adapter patterns, and improves the accuracy of social topic extraction and classification through semantic association-based extraction techniques and topic association-based classification techniques.

• International Journal of Knowledge Content Development & Technology
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• v.14 no.3
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• pp.115-140
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• 2024

With the advent of the Fourth Industrial Revolution and the era of big data, the ability to handle data has become essential. This has heightened the importance and necessity of data literacy competencies. The purpose of this study is to propose a framework for data literacy competencies. To achieve this goal, data literacy frameworks from eight countries and twelve pieces of literature on data literacy competencies were analyzed and synthesized, resulting in five categories and twenty-three competencies. The five categories are: data understanding and ethics, data collection and management, data analysis and evaluation, data utilization, and data governance and systems. It is hoped that the data literacy competency framework proposed in this study will serve as a foundational resource for policies, curricula, and the enhancement of individual data literacy competencies.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.956-961
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• 2009

When construction engineers perform their work in the jobsite, they have to record as-built conditions in the project log (Data Collection). On the other hand, the engineers often have to refer to the construction documents when necessary at the job faces (Data Access). The practice of Data Collection and Data Access in the jobsite can be greatly enhanced by utilizing mobile computing with wireless communications. In this paper, two cases of mobile computing applications for construction field management are presented; Mobile Specifications System and Mobile Data Collection System. The demonstration of the process for developing two mobile applications is the primary purpose of the paper. The problems and issues involved with adopting mobile computing for construction field are also presented. The simple information framework for mobile computing has been also proposed as an outcome of the research. As for development tools, readily available relational database and wireless network have been used. The use of commercial mobile broadband was examined for data communication where local area network is not available.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2222-2240
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• 2023

Brand fandom refers to a collection of consumers with strong emotions toward a brand. Studying the dynamics of brand fandom can help brands understand which services or strategies influence their consumers to become a part of brand fandom. However, existing literature on fandom in the last three decades has mainly used qualitative methods, and there is still a lack of research on fandom using quantitative methods. Specifically, previous studies lack a framework for locating fandoms from online textual data and analyzing their dynamics. This study proposes a framework for exploring brand fandom dynamics based on online textual data. This framework consists of four phases based on the design thinking model: Preparing Data, Defining Fandom Categories, Generating Fandom Dynamics, and Analyzing Fandom Dynamics. This framework uses techniques such as social network analysis and process mining, combined with brand personality theory. We demonstrate the applicability of this framework using case studies of two Korean home appliance brands. The dataset contains 14,593 posts by consumers in 374 online communities. The results show that the proposed framework can analyze brand fandom dynamics using textual customer data. Our study contributes to the interdisciplinary research at the intersection of data-driven service design and consumer culture quantification.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.942-945
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• 2005

This paper tried to analyze the collection and management method of shop-floor information for development of digital framework in u-manufacturing. In detail, the shop-floor information collection method through the direct communication with manufacturing devices using network Including RS-232C/422, field bus and ethernet is analyzed and proposed. In case the direct communication is impossible, the information collection method through additional sensors or data acquisition units is analyzed and proposed. Moreover, the collection method through bar code reader or touch screen of operators is analyzed and proposed to act up to machine to man/mobile/machine.

• Journal of Korean Society of Transportation
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• v.35 no.5
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• pp.409-422
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• 2017

TOD (Transit-Oriented Development) is one of the urban structure concentrated on the multifunctional space/district with public transportation system, which is introduced for maintaining sustainable future cities. With such trends, the project of building complex transferring centers located at a urban railway station has widely been spreaded and a comprehensive and systematic analytical framework is required to clarify and readily understand the complicated procedure of estimation with the large scale of the project. By doing so, this study is to develop a comprehensive analytical framework for estimating a pedestrian OD matrix using a spatial information and an integrated smart card data, which is so called a data depository and it has been applied to the Samseong station for the model validation. The proposed analytical framework contributes on providing a chance to possibly extend with digitalized and automated data collection technologies and a BigData mining methods.