• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.4
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• pp.33-43
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• 2016

Public transportation card data, which is collected for purposes of the Integrated Public Transportation Fare System, provides neither transfer time nor transfer frequency occurring on the metropolitan city-rail (MCR). And because there are no transfer toll gates installed on the MCR, data on transfers between lines are estimated through means such as elicitations using survey questionnaire, or otherwise through macroscopic observations, which poses the risk of transfer time and frequencies being underestimated. For the accurate estimation thereof, an explanation of the transit path that arises between the Entry-and Exit-Gates must be provided. The purpose of this research is twofold : 1) to build a transit path model to reflect the current state of transfer movements on the basis of transportation card reader data, and 2) to deduce information on transfers occurring in the greater metropolis. To achieve these aims, the idea of Big Nodes is introduced in the model to align transportation card reader operation system characteristics with those of the MCR network. The link-label method is applied in the model as well to make certain that the MCR network runs in an effective manner. Administrative information obtained by the transportation card reader is used to derive transfer time and frequency both in the city's mid-zones, and in the Seoul-Gyeonggi-Incheon district's large-zones. Public transportation card data from a single specific day in year 2014 is employed in the building of the quantified transfer specific data. Extended usage thereof as providing comprehensive data of transfer resistance on the MCR is also examined.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.5
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• pp.613-621
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• 2019

In general, transfer in subway stations is defined as transfer between lines and station transfer. In transfer between lines, passengers change from one subway line to another by utilizing horizontal pedestrian facilities such as transfer passages and pedestrian way. Station transfer appears in the situation that subway lines of enter and exit gate terminals differs from those of boarding and alighting trains and passenger trips utilize both vertical pedestrian facilities such as stair and escalator and horizontal facilities. The hypothesis on these two transfers presupposes that all subway lines are operated by either local train or express in subway network. This means that in a transfer case both local and express trains are operated in the same subway line, as a case of Seoul Metro Line 9, has not been studied. This research proposes a methodology of finding the same line transfer in the Seoul metropolitan subway network built based on the smart card network data by suggesting expanded network concept and a model that passengers choose a theirs minimum time routes.

• The Journal of Bigdata
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• v.7 no.1
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• pp.1-14
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• 2022

The proof-of-work consensus algorithm used by most blockchains is causing a massive waste of computing resources in the form of mining. A useful proof-of-work consensus algorithm has been studied to reduce the waste of computing resources in proof-of-work, but there are still resource waste and mining centralization problems when creating blocks. In this paper, the problem of resource waste in block generation was solved by replacing the relatively inefficient computation process for block generation with distributed artificial intelligence model learning. In addition, by providing fair rewards to nodes participating in the learning process, nodes with weak computing power were motivated to participate, and performance similar to the existing centralized AI learning method was maintained. To show the validity of the proposed methodology, we implemented a blockchain network capable of distributed AI learning and experimented with reward distribution through resource verification, and compared the results of the existing centralized learning method and the blockchain distributed AI learning method. In addition, as a future study, the thesis was concluded by suggesting problems and development directions that may occur when expanding the blockchain main network and artificial intelligence model.

• Information and Communications Magazine
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• v.31 no.2
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• pp.105-111
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• 2014

클라우드 컴퓨팅과 빅데이터 시대의 개막으로 클라우드에 저장되는 데이터가 급속도로 증가함에 따라 최근 클라우드 컴퓨팅의 주요한 요소로 클라우드 저장 시스템이 주목받고 있다. 클라우드 저장 시스템은 크게 두 가지 목적에 의해 동작한다. 첫 번째는 사용자에게 데이터를 소실 없이 정확하게 전달해주는 것이고, 두 번째는 네트워크 상에서 소실된 데이터를 복구해 내는 것이다. 데이터 소실은 분산 노드 내 장비의 결함, 소프트웨어 업데이트 등과 같은 요인에 의해 발생하는데, 이와 같은 데이터 소실에 대응하기 위해 소실 복구 코딩 기법을 사용한다. 본 고에서는 클라우드 저장 시스템의 요구사항들을 토대로 현재 클라우드 저장 시스템에 사용되는 다양한 코딩 기법을 살펴보고 차세대 클라우드 저장 시스템을 위한 코딩 기법에 대해 논의해본다.

• The Journal of Korean Association of Computer Education
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• v.18 no.6
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• pp.71-79
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• 2015

In a new era of Cloud and Big data, how to search the useful data from dynamic huge data pool in a right time and right way is most important at the stage where the information is getting more important. Above all, in the era of s Big Data it is required to design the advanced efficient intelligent Knowledge system which can process the dynamic variable big data. Accordingly in this paper we propose Dynamic personal Knowledge Network as one of the advanced Intelligent system approach. Adopting the human brain function and its neuro dynamics, an Intelligent system which has a structural flexibility was designed. For Structure-Function association, a personal Knowledge Network is made to be structured and to have reorganizing function as connecting the common nodes. We also design this system to have a reasoning process in the extracted optimal paths from the Knowledge Network.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.97-107
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• 2020

This study aims to conduct a comprehensive meta-study from the perspective of content analysis to explore trends in Korean academic research on the sharing economy by using the big data analytics. Comprehensive meta-analysis methodology can examine the entire set of research results historically and wholly to illuminate the tendency or properties of the overall research trend. Academic research related to the sharing economy first appeared in the year in which Professor Lawrence Lessig introduced the concept of the sharing economy to the world in 2008, but research began in earnest in 2013. In particular, between 2006 and 2008, research improved dramatically. In order to grasp the overall flow of domestic academic research of trends, 8 years of papers from 2013 to the present have been selected as target analysis papers, focusing on titles, keywords, and abstracts using database of electronic journals. Big data analysis was performed in the order of cleaning, analysis, and visualization of the collected data to derive research trends and insights by year and type of literature. We used Python3.7 and Textom analysis tools for data preprocessing, text mining, and metrics frequency analysis for key word extraction, and N-gram chart, centrality and social network analysis and CONCOR clustering visualization based on UCINET6/NetDraw, Textom program, the keywords clustered into 8 groups were used to derive the typologies of each research trend. The outcomes of this study will provide useful theoretical insights and guideline to future studies.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.291-306
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• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.28 no.3
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• pp.67-93
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• 2017

This study aims to investigate research trends on disaster safety Information based on the language network analysis methods. To accomplish it, we collected 312 Korean thesis and scholarly articles on disaster safety information published between 2008 and 2017 from RISS (Research Information Sharing Service) site. With the collected data, this study performed the statistical analysis based on bibliographic data. Also, this study performed the analysis of frequency and language network on keyword extracted from titles on the collected scholarly articles and thesis. This study found out that researches recently on Bigdata related to disaster safety information have been rapidly increased. Also, the needs of sharing and utilizing disaster safety information have increased. Also the various types of disaster safety information such as spatial data, real-time information, geographic information has been used for the disaster response.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.277-284
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• 2021

Recently, the smart home environment is expected to be a platform that collects, integrates, and utilizes various data through convergence with wireless information and communication technology. In fact, the number of smart devices with various sensors is increasing inside smart homes. The amount of data that needs to be processed by the increased number of smart devices is also increasing, and big data processing systems are actively being introduced to handle it effectively. However, traditional big data processing systems have all requests directed to cluster drivers before they are allocated to distributed nodes, leading to reduced cluster-wide performance sharing as cluster drivers managing segmentation tasks become bottlenecks. In particular, there is a greater delay rate on smart home devices that constantly request small data processing. Thus, in this paper, we design a Apriori-based big data system for effective data processing in smart home environments where frequent requests occur at the same time. According to the performance evaluation results of the proposed system, the data processing time was reduced by up to 38.6% from at least 19.2% compared to the existing system. The reason for this result is related to the type of data being measured. Because the amount of data collected in a smart home environment is large, the use of cache servers plays a major role in data processing, and association analysis with Apriori algorithms stores highly relevant sensor data in the cache.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.6
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• pp.1183-1190
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• 2017

With the development of network and IT technology, people are searching and purchasing items they want, not bounded by places. Therefore, there are various studies on how to solve the scalability problem due to the rapidly increasing data in the recommendation system. In this paper, we propose an item-based collaborative filtering method using Tag weight and a recommendation technique using MapReduce method, which is a distributed parallel processing method. In order to improve speed and efficiency, the proposed method classifies items into categories in the preprocessing and groups according to the number of nodes. In each distributed node, data is processed by going through Map-Reduce step 4 times. In order to recommend better items to users, item tag weight is used in the similarity calculation. The experiment result indicated that the proposed method has been more enhanced the appropriacy compared to item-based method, and run efficiently on the large amounts of data.