• Knowledge Management Research
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• v.19 no.1
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• pp.119-132
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• 2018

Many advanced products and services are emerging in the market thanks to data-based technologies such as Internet (IoT), Big Data, and AI. The construction of a system for data processing under the IoT network environment is not simple in configuration, and has a lot of restrictions due to a high cost for constructing a high performance server environment. Therefore, in this paper, we will design a development environment for large data analysis computing platform using open source with low cost and practicality. Therefore, this study intends to implement a big data processing system using Raspberry Pi, an ultra-small PC environment, and open source API. This big data processing system includes building a portable server system, building a web server for web mining, developing Python IDE classes for crawling, and developing R Libraries for NLP and visualization. Through this research, we will develop a web environment that can control real-time data collection and analysis of web media in a mobile environment and present it as a curriculum for non-IT specialists.

• Journal of Information Processing Systems
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• v.13 no.3
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• pp.518-532
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• 2017

Due to the rapid growth of the amount of data, research on bigdata processing has been highlighted. For bigdata processing, CUBRID Shard is able to support query processing in parallel way by dividing the database into a number of CUBRID servers. However, CUBRID Shard can answer a user's query only when the query is required to gain accesses to a single CUBRID server, instead of multiple ones. To solve the problem, in this paper we propose a CUBRID based distributed parallel query processing system that can answer a user's query in parallel and distributed manner. Finally, through the performance evaluation, we show that our proposed system provides 2-3 times better performance on query processing time than the existing CUBRID Shard.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.6
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• pp.259-266
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• 2021

Edge Computing enables real-time big data processing by performing computing close to the physical location of the user or data source. However, in an edge computing environment, various situations that affect big data processing performance may occur depending on temporary service requirements or changes of physical resources in the field. In this paper, we proposed a BigCrawler system that dynamically configures the computing module and storage module according to the big data collection status and computing resource usage status in the edge computing environment. And the feature of big data processing workload according to the arrangement of computing module and storage module were analyzed.

• The Journal of Bigdata
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• v.5 no.1
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• pp.89-96
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• 2020

Deep learning using an artificial neural network has been recently researched and developed in various fields such as image recognition, big data and data analysis. However, federated learning has emerged to solve issues of data privacy invasion and problems that increase the cost and time required to learn. Federated learning presented learning techniques that would bring the benefits of distributed processing system while solving the problems of existing deep learning, but there were still problems with server-client system and motivations for providing learning data. So, we replaced the role of the server with a blockchain system in federated learning, and conducted research to solve the privacy and security problems that are associated with federated learning. In addition, we have implemented a blockchain-based system that motivates users by paying compensation for data provided by users, and requires less maintenance costs while maintaining the same accuracy as existing learning. In this paper, we present the experimental results to show the validity of the blockchain-based system, and compare the results of the existing federated learning with the blockchain-based federated learning. In addition, as a future study, we ended the thesis by presenting solutions to security problems and applicable business fields.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.201-209
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• 2021

The term, eGovernmen or e-Government, uses technology communications devices such as computers and the Internet to provide public services to citizens and others. The eGovernment or e-government provides citizens with new opportunities to access the government directly and conveniently, while the government provides citizens with directservices. Also, in these days, cloud computing is a feature that enables users to use computer system resources, especially data storage (cloud storage) and on-demand computing power, without having to manage themselves. The term is commonly used to describe data centers that are available to many users over the Internet. Today, the dominant Big Cloud is distributed across multiple central servers. You can designate it as an Edge server if it is relatively close to the user. However, despite the prevalence of e-government and cloud computing, each of these concepts has evolved. Research attempts to combine these two concepts were not being made properly. For this reason, in this work, we aim to produce independent and objective analysis results by separating progress steps for the analysis of e-government cloud service platforms. This work will be done through an analysis of the development process and architectural composition of the e-government development standard framework and the cloud platform PaaS-TA. In addition, this study is expected to derive implications from an analysis perspective on the direction and service composition of the e-government cloud service platform currently being pursued.

• The Journal of Bigdata
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• v.3 no.1
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• pp.73-82
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• 2018

At the core of future medicine is the realization of Precision Medicine centered on individuals. For this, we need to have an open ecosystem that can view, manage and distribute healthcare data anytime, anywhere. However, since healthcare data deals with sensitive personal information, a significant level of reliability and security are required at the same time. In order to solve this problem, the healthcare industry is paying attention to the blockchain technology. Unlike the existing information communication infrastructure, which stores and manages transaction information in a central server, the block chain technology is a distributed operating network in which a data is distributed and managed by all users participating in the network. In this study, we not only discuss the technical and legal aspects necessary for demonstration of healthcare data distribution using blockchain technology but also introduce KOREN SDI Network-based Healthcare Big Data Distribution Demonstration Study. In addition, we discuss policy strategies for activating blockchain technology in healthcare.

• The Journal of Bigdata
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• v.4 no.2
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• pp.207-220
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• 2019

Recent developments in information and communication technology has enabled the deployment of sensor based data to provide real-time services. In Korea, The Korea Transportation Safety Authority is collecting driving information of all commercial vehicles through a fitted digital tachograph (DTG). This information gathered using DTG can be utilized in various ways in the field of transportation. Notably in autonomous driving, the real-time analysis of this information can be used to prevent or respond to dangerous driving behavior. However, there is a limit to processing a large amount of data at a level suitable for real-time services using a traditional database system. In particular, due to a such technical problem, the processing of large quantity of traffic big data for real-time commercial vehicle operation information analysis has never been attempted in Korea. In order to solve this problem, this study optimized the new database server system and confirmed that a real-time service is possible. It is expected that the constructed database system will be used to secure base data needed to establish digital twin and autonomous driving environments.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.73-85
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• 2022

In the era of the 4th industrial revolution, where automation and connectivity are maximized with artificial intelligence, the importance of data collection and utilization for model update is increasing. In order to create a model using artificial intelligence technology, it is usually necessary to gather data in one place so that it can be updated, but this can infringe users' privacy. In this paper, we introduce federated learning, a distributed machine learning method that can update models in cooperation without directly sharing distributed stored data, and introduce a study to optimize distributed consensus among participants without an existing server. In addition, we propose a pattern and group-based distributed consensus optimization algorithm that uses an algorithm for generating patterns and groups based on the Kirkman Triple System, and performs parallel updates and communication. This algorithm guarantees more privacy than the existing distributed consensus optimization algorithm and reduces the communication time until the model converges.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1129-1139
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• 2015

Inference process generates additional triples from knowledge represented in RDF triples of semantic web technology. Tens of million of triples as an initial big data and the additionally inferred triples become a knowledge base for applications such as QA(question&answer) system. The inference engine requires more computing resources to process the triples generated while inferencing. The additional computing resources supplied by underlying resource pool in cloud computing can shorten the execution time. This paper addresses an algorithm to allocate the number of computing nodes "elastically" at runtime on Hadoop, depending on the size of knowledge data fed. The model proposed in this paper is composed of the layered architecture: the top layer for applications, the middle layer for distributed parallel inference engine to process the triples, and lower layer for elastic Hadoop and server visualization. System algorithms and test data are analyzed and discussed in this paper. The model hast the benefit that rich legacy Hadoop applications can be run faster on this system without any modification.

• The Journal of Bigdata
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• v.9 no.1
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• pp.101-113
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• 2024

Federated learning has garnered attention as an efficient method for training machine learning models in a distributed environment while maintaining data privacy and security. This study proposes a novel FedRFBagging algorithm to optimize the performance of random forest models in such federated learning environments. By dynamically adjusting the trees of local random forest models based on client-specific data characteristics, the proposed approach reduces communication costs and achieves high prediction accuracy even in environments with numerous clients. This method adapts to various data conditions, significantly enhancing model stability and training speed. While random forest models consist of multiple decision trees, transmitting all trees to the server in a federated learning environment results in exponentially increasing communication overhead, making their use impractical. Additionally, differences in data distribution among clients can lead to quality imbalances in the trees. To address this, the FedRFBagging algorithm selects only the highest-performing trees from each client for transmission to the server, which then reselects trees based on impurity values to construct the optimal global model. This reduces communication overhead and maintains high prediction performance across diverse data distributions. Although the global model reflects data from various clients, the data characteristics of each client may differ. To compensate for this, clients further train additional trees on the global model to perform local optimizations tailored to their data. This improves the overall model's prediction accuracy and adapts to changing data distributions. Our study demonstrates that the FedRFBagging algorithm effectively addresses the communication cost and performance issues associated with random forest models in federated learning environments, suggesting its applicability in such settings.