• Journal of Korea Technology Innovation Society
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• v.19 no.1
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• pp.80-104
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• 2016

Existing methodologies of technology planning about promising new technology focused on target technology itself, so it is true that socio-environmental context which the relevant technology has influence on is not well understood. In this respect, this study is aimed to questingly examine that news content analysis methodologies widely available in the field of science communication can be applied as a complementary methodology for contextual understanding of socio-environment in terms of technology planning about promising new technology. In the co-evolutionary environment of technology-society, promising new technology shows hype phenomenon regarding the relation with the society. Based on this, this study performed news content analysis and examined if the consequences of analysis would match hype cycle. It tried to explore substantive content understanding by socio-environment factors according to specific news frame content. To do this, new content analysis was performed targeting cloud computing as a representative promising new technology. The result of news content analysis targeting general newspapers, business news, IT special newspapers revealed that the tendency of news reporting matched the trend of hype cycle. Particularly, it was verified that reporting attitude and news frame analysis provided useful information to understand contextual content depending on social, economic, and cultural environment factors about promising new technology. The results of this study implied that news content analysis could overcome the limitation of technology information analysis focusing on academic journal patent usually applied for technology planning and could be used as a complementary methodology for understanding the context depending on macro-environment factors. In conclusion, application of news content analysis on the phase of macro-environment analysis of technology planning could contribute to the securement of mutually balanced view in the co-evolutionary perspective of technology-society.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.21-33
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• 2022

Recently, the recommender system has been widely used in various fields such as movies, music, online shopping, and social media, and in the meantime, the recommender model has been developed from correlation analysis through the Apriori model, which can be said to be the first-generation model in the recommender system field. In 2005, many models have been proposed, including deep learning-based models, which are receiving a lot of attention within the recommender model. The recommender model can be classified into a collaborative filtering method, a content-based method, and a hybrid method that uses these two methods integrally. However, these basic methods are gradually losing their status as methodologies in the field as they fail to adapt to internal and external changing factors such as the rapidly changing user-item interaction and the development of big data. On the other hand, the importance of deep learning methodologies in recommender systems is increasing because of its advantages such as nonlinear transformation, representation learning, sequence modeling, and flexibility. In this paper, among deep learning methodologies, RNN, CNN, and GAN-based models suitable for sequential modeling that can accurately and flexibly analyze user-item interactions are classified, compared, and analyzed.

• KIISE Transactions on Computing Practices
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• v.24 no.1
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• pp.24-32
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• 2018

This paper proposes a Big Data system for energy Big Data which is aggregated in real-time from industrial and public sources. The constructed Big Data system is based on Hadoop and the Spark framework is simultaneously applied on Big Data processing, which supports in-memory distributed computing. In the paper, we focus on Big Data, in the form of heat energy for district heating, and deal with methodologies for storing, managing, processing and analyzing aggregated Big Data in real-time while considering properties of energy input and output. At present, the Big Data influx is stored and managed in accordance with the designed relational database schema inside the system and the stored Big Data is processed and analyzed as to set objectives. The paper exemplifies a number of heat demand plants, concerned with district heating, as industrial sources of heat energy Big Data gathered in real-time as well as the proposed system.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.39-46
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• 2015

Knowledge space theory is a theory that provides a guidelines for human learners' possible education decisions and has been used in various educational environment. However, traditional methodologies using the knowledge space theory have always depended on handwork system and it is necessary to learn programming language such as Visual Basic and R, causing time consuming situations. In order to overcome those issues on the environment of education we propose a new Knowledge Structure Analysis System that not just analyzes learners' knowledge structures automatically but to provide critical learning path for the learners based on knowledge space theory. Proposed system is implemented by using rApache generating critical learning path computing Chi-square value. This provides an automatic way of analyzing knowledge structure in learners' knowledge space and shows systematic reviews for the knowledge space.

• Journal of Internet Computing and Services
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• v.10 no.4
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• pp.127-138
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• 2009

Mobile web applications are being used and changed rapidly due to the growth of mobile device performance. But, cost of development environment and standards make the high development cost and low productivity. It is main reason that the design and implementation of the applications are more time consuming than general computing environments. In this paper, we propose MWeb(MobileWeb)-Framework based on the agile methodology and Ruby on Rails that is a kind of framework for supporting mobile web application development using mobile web standards. This work consists of the mobile web development architecture and agile process model. MWeb-Framework will support the same user experience to the different devices. We validates the framework by implementing the case studies through suggested mobile web development framework. As a result, we can develop the mobile web applications with productivity and quality. In the future, we will suggest how to make the MWeb-Framework standardization and practically apply the frameworks the various case studies to improve framework potentially problems.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.475-478
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• 2001

Currently, in spite of continual efforts the information utility of construction industry has low-efficiency for the other industry. These problems arose from the erroneous efforts for the information utility of construction industry. Because it left out of consideration of the construction characters, which are the center of construction sites, and this information system was directly introduced and operated from the other industry. Also, we can point out mistake that the existing information systems disregarded using facility in the fixing of these problems. Therefore, this study describes a new process, which purposes to build and utilize construction information management system as using Personal Digital Assistant (PDA) of the mobile communication concept, to solve the problems of existing construction information management system.

• The Journal of Information Systems
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• v.27 no.4
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• pp.1-33
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• 2018

Purpose The purpose of this study is to review topic and survey methodological trends in 'The Journal of Information Systems' in order to present the practical guidelines for the future IS research. By attempting to conduct a meta-analysis on both topic and survey methodological trends, this study could provide researchers wishing to pursue this line of work further with what can be done to improve IS disciplines. Design/methodology/approach In this study, we have reviewed 185 papers that were published in 'The Journal of Information Systems' from 2010 to 2018 and classified them based on topics studied and survey methodologies used. The classification guidelines, which was developed by Palvia et al.(2015), has been used to capture the topic trends. We have also employed Struab et al.(2004)s' guidelines for securing rigor of validation issues. By using two guidelines, this study could also present topic and rigor trends in 'The Journal of Information Systems' and compare them to those trends in International Journals. Findings Our findings have identified dominant research topics in 'The Journal of Information Systems'; 1) social media and social computing, 2) IS usage and adoption, 3) mobile computing, 4) electronic commerce/business, 5) security and privacy, 6) supply chain management, 7) innovation, 8) knowledge management, and 9) IS management and planning. This study also could offer researchers who pursue this line of work further practical guidelines on mandatory (convergent and discriminant validity, reliability, and statistical conclusion validity), highly recommended (common method bias testing), and optional validations (measurement invariance testing for subgroup analysis, bootstrapping methods for testing mediating effects).

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.315-319
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• 2022

Cloud computing has emerged to be the most effective headway for investigating crime especially cybercrime in this modern world. Even as we move towards an information technology-controlled world, it is important to note that when innovations are made, some negative implications also come with it, and an example of this is these criminal activities that involve technology, network devices, and networking that have emerged as a result of web improvements. These criminal activities are the ones that have been termed cybercrime. It is because of these increased criminal activities that organizations have come up with different strategies that they use to counter these crimes, and one of them is carrying out investigations using the cloud environment. A cloud environment has been defined as the use of web-based applications that are used for software installation and data stored in computers. This paper examines problems that are a result of cybercrime investigation in the cloud environment. Through analysis of the two components in play; cybercrime and cloud environment, we will be able to understand what are the problems that are encountered when carrying out investigations in cloud forensics. Through the use of secondary research, this paper found out that most problems are associated with technical and legal channels that are involved in carrying out these investigations. Investigator's mistakes when extracting pieces of evidence form the most crucial problems that take a lead when it comes to cybercrime investigation in the cloud environment. This paper not only flags out the challenges that are associated with cybercrime investigation in cloud environments but also offer recommendations and suggested solutions that can be used to counter the problems in question here. Through a proposed model to perform forensics investigations, this paper discusses new methodologies solutions, and developments for performing cybercrime investigations in the cloud environment.

• Geomechanics and Engineering
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• v.37 no.3
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• pp.223-241
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• 2024

Evaluating the performance of Tunnel Boring Machines (TBMs) stands as a pivotal juncture in the domain of hard rock mechanized tunneling, essential for achieving both a dependable construction timeline and utilization rate. In this investigation, three advanced artificial neural networks namely, gated recurrent unit (GRU), back propagation neural network (BPNN), and simple recurrent neural network (SRNN) were crafted to prognosticate TBM-rate of penetration (ROP). Drawing from a dataset comprising 1125 data points amassed during the construction of the Alborze Service Tunnel, the study commenced. Initially, five geomechanical parameters were scrutinized for their impact on TBM-ROP efficiency. Subsequent statistical analyses narrowed down the effective parameters to three, including uniaxial compressive strength (UCS), peak slope index (PSI), and Brazilian tensile strength (BTS). Among the methodologies employed, GRU emerged as the most robust model, demonstrating exceptional predictive prowess for TBM-ROP with staggering accuracy metrics on the testing subset (R² = 0.87, NRMSE = 6.76E-04, MAD = 2.85E-05). The proposed models present viable solutions for analogous ground and TBM tunneling scenarios, particularly beneficial in routes predominantly composed of volcanic and sedimentary rock formations. Leveraging forecasted parameters holds the promise of enhancing both machine efficiency and construction safety within TBM tunneling endeavors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2253-2280
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• 2024

In the upcoming era of transportation, a groundbreaking technology, known as vehicle-to-everything (V2X) communication, is poised to redefine our driving experience and revolutionize traffic management. Real-time and secure communication plays a pivotal role in V2X networks, with the decision-making process being a key factor in establishing communication and determining malicious nodes. The proposed framework utilizes a directed acyclic graph (DAG) to facilitate real-time processing and expedite decision-making. This innovative approach ensures seamless connectivity among vehicles, the surrounding infrastructure, and various entities. To enhance communication efficiency, the entire roadside unit (RSU) region can be subdivided into various sub-regions, allowing RSUs to monitor and govern each sub-region. This strategic approach significantly reduces transaction approval time, thereby improving real-time communication. The framework incorporates a consensus mechanism to ensure robust security, even in the presence of malicious nodes. Recognizing the dynamic nature of V2X networks, the addition and removal of nodes are aligned. Communication latency is minimized through the deployment of computational resources near the data source and leveraging edge computing. This feature provides invaluable recommendations during critical situations that demand swift decision-making. The proposed architecture is further validated using the "veins" simulation tool. Simulation results demonstrate a remarkable success rate exceeding 95%, coupled with a significantly reduced consensus time compared to prevailing methodologies. This comprehensive approach not only addresses the evolving requirements of secure V2X communication but also substantiates practical success through simulation, laying the foundation for a transformative era in transportation.