• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.237-252
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• 2017

In recent years, data centers have become the core infrastructure to deal with big data processing. For these big data applications, network transmission has become one of the most important factors affecting the performance. In order to improve network utilization and reduce job completion time, in this paper, by real-time monitoring from the application layer, we propose job-aware priority scheduling. Our approach takes the correlations of flows in the same job into account, and flows in the same job are assigned the same priority. Therefore, we expect that flows in the same job finish their transmissions at about the same time, avoiding lagging flows. To achieve load balancing, two approaches (Flow-based and Spray) using ECMP (Equal-Cost multi-path routing) are presented. We implemented our scheme using NS-2 simulator. In our evaluations, we emulate real network environment by setting background traffic, scheduling delay and link failures. The experimental results show that our approach can enhance the Hadoop job execution efficiency of the shuffle stage, significantly reduce the network transmission time of the highest priority job.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3028-3039
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• 2015

Recently, the web index of Korea peaked at the top 10 among the eighty six countries, and Korea became the only Asian country ranked at the top level. Korea also has been on the top in the field of Internet penetration rate, in terms of both high-speed broadband and wireless Internet. However, such achievements did not guarantee the national level for the effective use of information utilizing Internet. According to OECD, the national informatization index of Korea has not been free from the middle of the OECD countries. Despite of the heightened pressure in practically enhancing effective information use utilizing Internet, the previous research interests and efforts to develop the Internet-related framework or to identify Internet capabilities rarely existed. The study aims to propose the framework, named "Internet Information Orientation" that illustrates the relationship between Internet capabilities and national competitiveness on Internet. The research identified the specific Internet capabilities, reclassified the capabilities based on the research issues provided at the 6^th international conference on Internet held in December 2014, and finally described the rigorous research endeavors on the issues. As a result, 16 papers presented and selected as the outstanding papers at the conference handle issues to be brought together, which include: Wireless Network, Internet of Things, Green Computing, Multimedia Processing, Big Data and Text Mining, Database in Cloud Environment, Business Intelligence, Software Engineering, IT Strategy & Policy, and Social Network Services.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5243-5263
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• 2017

Data center networks provide critical bandwidth for the continuous growth of cloud computing, multimedia storage, data analysis and other businesses. The problem of low link bandwidth utilization in data center network is gradually addressed in more hot fields. However, the current scheduling strategies applied in data center network do not adapt to the real-time dynamic change of the traffic in the network. Thus, they fail to distribute resources due to the lack of intelligent management. In this paper, we present an improved adaptive traffic scheduling strategy utilizing the simulated annealing genetic algorithm (SAGA). Inspired by the idea of software defined network, when a flow arrives, our strategy changes the bandwidth demand dynamically to filter out the flow. Then, SAGA distributes the path for the flow by considering the scheduling of the different pods as well as the same pod. It is implemented through software defined network technology. Simulation results show that the bisection bandwidth of our strategy is higher than state-of-the-art mechanisms.

• The Research Journal of the Costume Culture
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• v.22 no.2
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• pp.313-325
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• 2014

This study seeks to establish a ubiquitous wardrobe with a clothing management function through detailed and subdivided integration research. To create a database, a survey was conducted, and the output data were analyzed and used as the basic data. In particular, this study researched clothing management programs and companies' standards of clothing product classification systems. First, through an in-depth analysis centering on specialists, we established the contents of a ubiquitous wardrobe and used the concept of cloud computing to support the wardrobe contents and smart phone applications. Second, this research found significant differences between individuals, schools, and enterprises in their applications of, and the importance they attach to, design images. A detailed database composed of various categories was established to present the ubiquitous wardrobe contents with efficient functions. Third, we facilitated the search process by designating clothes with QR codes, which is one of the functions of contents. Fourth, the code numbers generated in the process of entering clothes into the database were utilized as RFID information as a way to arrange the products in a simpler manner. The ubiquitous wardrobe was constructed as a web-style hybrid, and its contents areavailable through mobile applications and QR codes.

• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.79-82
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• 2010

다중서열정렬 알고리즘은 생명정보학 분야에서 서열기반의 계통분류 분석에 가장 많이 사용되며, 가장 대표적인 공개 프로그램은 ClustalW로 사용자가 로컬시스템에 설치하여 이용할 수 있다. 그러나 실제로 사용자들이 ClustalW을 설치한 후, 서열데이터의 준비, 가공, 처리 및 타 시스템과 연동 등과 같은 작업을 하는데 여러 가지 어려움이 있다. 따라서 본 논문에서는 다중서열정렬 작업을 편리하고 빠르게 수행할 수 있는 웹기반의 고성능 다중서열정렬시스템을 제안한다. 제안된 시스템의 특징은, (1) Inter-Query 라우팅 알고리즘을 통해 다수의 PC 자원을 효율적으로 활용하여 계산 성능을 극대화하였으며, (2) 사용자 편의성을 고려한 웹인터페이스의 제공을 통해 개인화된 데이터관리, 실시간 모니터링, 데이터 편집 등을 지원하여 사용자가 손쉽게 서열데이터의 수집, 관리 및 처리할 수 있도록 지원한다.

• Journal of the Ergonomics Society of Korea
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• v.31 no.4
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• pp.577-583
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• 2012

Objective: This paper describes the process and results related to the development of gesture recognition-based natural user interface(NUI) for vehicle maintenance e-Training system. Background: E-Training refers to education training that acquires and improves the necessary capabilities to perform tasks by using information and communication technology(simulation, 3D virtual reality, and augmented reality), device(PC, tablet, smartphone, and HMD), and environment(wired/wireless internet and cloud computing). Method: Palm movement from depth camera is used as a pointing device, where finger movement is extracted by using OpenCV library as a selection protocol. Results: The proposed NUI allows trainees to control objects, such as cars and engines, on a large screen through gesture recognition. In addition, it includes the learning environment to understand the procedure of either assemble or disassemble certain parts. Conclusion: Future works are related to the implementation of gesture recognition technology for a multiple number of trainees. Application: The results of this interface can be applied not only in e-Training system, but also in other systems, such as digital signage, tangible game, controlling 3D contents, etc.

• KIPS Transactions on Computer and Communication Systems
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• v.1 no.2
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• pp.103-108
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• 2012

Recently, IT Governance has been applied to business management environment. In this paper, we study business model that can minimize information security risk using IT governance in cloud computing environment. Especially, we propose the interaction model that link risk management for subject of information security governance. In our model, synergy means the effective, strategic and secure business support. And interaction analysis of BMIS's 4 elements and 6 dynamic interconnections is required. Therefore we propose interaction model which can link risk management based on COSO ERM or COBIT Risk IT Framework.

• Smart Structures and Systems
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• v.24 no.6
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• pp.745-757
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• 2019

Road surface deteriorations such as potholes have caused motorists heavy monetary damages every year. However, effective road condition monitoring has been a continuing challenge to road owners. Depth cameras have a small field of view and can be easily affected by vehicle bouncing. Traditional image processing methods based on algorithms such as segmentation cannot adapt to varying environmental and camera scenarios. In recent years, novel object detection methods based on deep learning algorithms have produced good results in detecting typical objects, such as faces, vehicles, structures and more, even in scenarios with changing object distances, camera angles, lighting conditions, etc. Therefore, in this study, a Deep Learning Pothole Detector (DLPD) based on the deep region-based convolutional neural network is proposed for autonomous detection of potholes from images. About 900 images with potholes and road surface conditions are collected and divided into training and testing data. Parameters of the network in the DLPD are calibrated based on sensitivity tests. Then, the calibrated DLPD is trained by the training data and applied to the 215 testing images to evaluate its performance. It is demonstrated that potholes can be automatically detected with high average precision over 93%. Potholes can be differentiated from manholes by training and applying a manhole-pothole classifier which is constructed using the convolutional neural network layers in DLPD. Repeated detection of the same potholes can be prevented through feature matching of the newly detected pothole with previously detected potholes within a small region.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.3068-3085
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• 2021

The economics of big data knowledge, especially cloud computing and statistical data of consumer preferences, has attracted increasing academic and industry practitioners' attention. Firms nowadays require purchasing not only external private patent knowledge from other firms, but also proprietary big data knowledge to support their new product development. Extant research investigates pricing strategies of external private patent knowledge and proprietary big data knowledge separately. Yet, a comprehensive investigation of pricing strategies of these two types of knowledge is in pressing need. This research constructs an overarching pricing model of external private patent knowledge and proprietary big data knowledge through the lens of firm profitability as a knowledge transaction recipient. The proposed model can help those firms who purchase external knowledge choose the optimal knowledge structure and pricing strategies of two types of knowledge, and provide theoretical and methodological guidance for knowledge transaction recipient firms to negotiate with knowledge providers.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1036-1054
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• 2019

Recently, concomitant with a surge in numbers of Internet of Things (IoT) devices with various sensors, mobile crowdsensing (MCS) has provided a new business model for IoT. For example, a person can share road traffic pictures taken with their smartphone via a cloud computing system and the MCS data can provide benefits to other consumers. In this service model, to encourage people to actively engage in sensing activities and to voluntarily share their sensing data, providing appropriate incentives is very important. However, the sensing data from personal devices can be sensitive to privacy, and thus the privacy issue can suppress data sharing. Therefore, the development of an appropriate privacy protection system is essential for successful MCS. In this study, we address this problem due to the conflicting objectives of privacy preservation and incentive payment. We propose a privacy-preserving mechanism that protects identity and location privacy of sensing users through an on-demand incentive payment and group signatures methods. Subsequently, we apply the proposed mechanism to one example of MCS-an intelligent parking system-and demonstrate the feasibility and efficiency of our mechanism through emulation.