• Journal of Digital Convergence
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• v.20 no.4
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• pp.791-797
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• 2022

Through recent metaverse marketing case studies, success factors and activation methods were analyzed from the perspective of content, platform, network, and device of the metaverse ecosystem in each industry. The importance of contents and platform of metaverse could be found in entertainment, fashion, office space and real estate, education, advertisement and commerce industries. In order to vitalize the metaverse, firstly, it is necessary to strengthen active participation and retention by providing a stable revenue model for market participants. Secondly, the importance of attractive content to expand subscribers is a key trigger for metaverse activation. Thirdly, it is necessary to increase the convenience of using metaverse service by using a light and simple device for the user. Fourthly, a win-win cooperation strategy should be supported in the value chain of the industry through ecosystem scalability. In addition, business opportunities for market participants and additional revenue models should be continuously provided.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.331-337
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• 2022

Smart learning is augmented with digital, context-aware, and adaptable technologies to encourage students to learn better and faster. To ensure that digital learning is successful and that implementation is efficient, it is critical that the dimensions of digital learning are arranged correctly and that interactions between the various elements are merged in an efficient and optimal manner. This paper builds and discusses a basic framework for smart learning in the digital age, aimed to improve students' abilities and performance in learning. The proposed framework consists of five dimensions: Teacher, Technology, Learner, Digital content, and Evaluation. The Teacher and Learner dimensions operate on two levels: (a) an abstract level to fit in knowledge and skills or interpersonal characteristics and (b) a concrete level in the form of digital devices used by teachers and learners. Moreover, this paper proposes asynchronous online course delivery model. An Arabic smart learning platform has been developed, based on these smart learning core dimensions and the asynchronous online course delivery model, because despite the official status of this language in many countries, there is a lack of Arabic platforms to teach Arabic. Moreover, many non-native Arabic speakers around the world have expressed an interest in learning it. The Arabic digital platform consists of over 70 lessons classified into three competence levels: beginner, intermediate, and advanced, delivered by Arabic experts and Arabic linguists from various Arab countries. The five dimensions are described for the Arabic platform in this paper. Learner dimension is the Arabic and non-Arabic speakers, Teacher dimension is Arabic experts and Arabic linguistics, Technology dimension consists of technology for Arabic platform that includes web design, cloud computing, big data, etc. The digital contents dimension consists of web-based video, records, etc. The evaluation dimension consists of Teachers rating, comments, and surveys.

• Journal of Korean Society of Disaster and Security
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• v.17 no.1
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• pp.17-25
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• 2024

Recent climate change has led to an increase in the probability of forest disasters (forest fires, landslides). However, disaster systems providing information for forest disaster response lack unified information provision. Therefore, this study aims to provide essential disaster information from a unified system for swift disaster response. To achieve this goal, we conducted a fundamental study on the necessary components for designing a forest disaster platform, explored methods for visualizing platforms enabling swift response and information provision during forest disasters through case studies, and presented the findings. Our results indicate that both domestic and international forest disaster response platforms commonly utilize spatial information to provide location-specific information. Key components identified for designing a response platform for forest disasters include constructing forest disaster big data, including climate information for target areas, developing technology for integrated diagnosis of forest disasters at each stage, and designing tailored safety care services for disaster areas.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.669-678
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• 2013

Recently, because the portable devices became popular, it is easily to see that each person carries more than just one portable device and the use of the smartphone stretches as time goes by. After the smartphone has propagated rapidly, the total usage of the smartphone applications has also increased. But still, each application store has a different platform to develop and to apply an application. The application store is divided into two big markets, the Android and the Apple. So the developers have to develop their application by using these two different platforms. Developing into two different platforms almost makes a double development cost. And for the other platforms, the weakness is, which still have a small market breadth like Bada is not about the cost, but about drawing the proper developers for the given platform application development. The web application is rising up as the solution to solve these problems, reducing the cost and time in developing applications for every platform. For web applications don't need to make a vassal relationship with application markets platform. Which makes it possible for an application to operate properly in every portable devices and reduces the time and cost in developing. Therefore, all of the application markets could be united into one big market through a protocol which will connect each web applications market. But, still there is no standard for the web application store and no current web application store is possible to interlock with other web application stores. In this paper, we are trying to suggest a protocol by developing a prototype and prove that this protocol can supplement the current weakness.

• Journal of the Korea Convergence Society
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• v.8 no.4
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• pp.19-24
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• 2017

The growing number of electronic financial transactions (e-banking) has entailed the rapid increase in security threats such as extortion and falsification of financial transaction data. Against such background, rigid security and countermeasures to hedge against such problems have risen as urgent tasks. Thus, this study aims to implement an improved case model by applying the Fraud Detection System (hereinafter, FDS) in a financial corporation 'A' using big data technique (e.g. the function to collect/store various types of typical/atypical financial transaction event data in real time regarding the external intrusion, outflow of internal data, and fraud financial transactions). As a result, There was reduction effect in terms of previous scenario detection target by minimizing false alarm via advanced scenario analysis. And further suggest the future direction of the enhanced FDS.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1571-1576
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• 2022

This study aims to analyze the interest of modern people in non-face-to-face medical counseling in the medical industrys. Big data was collected on two social platforms, 지식인, a platform that allows experts to receive medical counseling, and YouTube. In addition to the top five keywords of telephone counseling, "internal medicine", "general medicine", "department of neurology", "department of mental health", and "pediatrics", a data set was built from each platform with a total of eight search terms: "specialist", "medical counseling", and "health information". Afterwards, pre-processing processes such as morpheme classification, disease extraction, and normalization were performed based on the crawled data. Data was visualized with word clouds, broken line graphs, quarterly graphs, and bar graphs by disease frequency based on word frequency. An emotional classification model was constructed only for YouTube data, and the performance of GRU and BERT-based models was compared.

• Korean Journal of Applied Biomechanics
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• v.17 no.4
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• pp.17-25
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• 2007

The purpose of the study is to give basic data for the improvement of the skill and to show an exemplary position for squash club members or trainers thru a comparative analysis on the kinematics and kinetics variables on the forehand drive motion in playing squash. The objects of the research are divided into two sections, skilled group(n=8) and unskilled group(n=8). The skilled group is composed of professional players currently working and unskilled group is career of six month, both of lives in B city. In this research, to gather the data 3D motion analysis and test result analysis using force platform was used. The variables are duration, position, segment velocity, segment acceleration and etc. in using force platform. The results are as follows: 1. The duration per phase of the skilled is 0.18sec P1(DS) while that of unskilled is 0.32sec. in P2(FT), the duration of the skilled is 0.29sec, that of unskilled is 0.34sec. Average of the duration of the skilled is 0.48sec, while the unskilled, 0.66sec. 2. Regarding positional movements per event, the unskilled has a relatively higher position in center of gravity, shoulder joint, elbow joint compared with that of the skilled. Generally speaking, positions of the unskilled is higher than the skilled. 3. In segment velocity per event, R-shank, R-upper arm, R-forearm and racket. The skilled is faster than the unskilled. we found a big dig difference in shank. 4. In acceleration per event, there was a big difference in upper-arm and fore-arm of the impact. 5. The skilled group on the force platform shows relatively stable and regular changes while the unskilled shows unstable from the touch down to initial 20% the force value of central support period after the impact moment decreases rapidly and the center of gravity is not moved well. 6. The maximum force value of the skilled is 1019.7N. it is found 19.86% of the total duration. That of the unskilled is 639.2N, it is found 20.67% of total duration.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4717-4737
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• 2017

Today, smart grids, smart homes, smart water networks, and intelligent transportation, are infrastructure systems that connect our world more than we ever thought possible and are associated with a single concept, the Internet of Things (IoT). The number of devices connected to the IoT and hence the number of traffic flow increases continuously, as well as the emergence of new applications. Although cutting-edge hardware technology can be employed to achieve a fast implementation to handle this huge data streams, there will always be a limit on size of traffic supported by a given architecture. However, recent cloud-based big data technologies fortunately offer an ideal environment to handle this issue. Moreover, the ever-increasing high volume of traffic created on demand presents great challenges for flow management. As a solution, flow aggregation decreases the number of flows needed to be processed by the network. The previous works in the literature prove that most of aggregation strategies designed for smart grids aim at optimizing system operation performance. They consider a common identifier to aggregate traffic on each device, having its independent static aggregation policy. In this paper, we propose a dynamic approach to aggregate flows based on traffic characteristics and device preferences. Our algorithm runs on a big data platform to provide an end-to-end network visibility of flows, which performs high-speed and high-volume computations to identify the clusters of similar flows and aggregate massive number of mice flows into a few meta-flows. Compared with existing solutions, our approach dynamically aggregates large number of such small flows into fewer flows, based on traffic characteristics and access node preferences. Using this approach, we alleviate the problem of processing a large amount of micro flows, and also significantly improve the accuracy of meeting the access node QoS demands. We conducted experiments, using a dataset of up to 100,000 flows, and studied the performance of our algorithm analytically. The experimental results are presented to show the promising effectiveness and scalability of our proposed approach.

• Journal of the Korean Society of Earth Science Education
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• v.14 no.1
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• pp.33-47
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• 2021

This study developed a big data-based STEAM (Science, Technology, Engineering, Art & Mathematics) program using WWT (World Wide Telescope), focusing on content elements of 'solar system', 'star and universe' in the 2015 revised science curriculum, and in order to find out the effectiveness of the STEAM program, analyzed creative problem solving, STEAM attitude, and STEAM satisfaction by applying it to one middle school 176 students simple random sampled. The results of this study are as follows. First, we developed a program to encourage students to actively and voluntarily participating, utilizing the astronomical data platform WWT. Second, in the paired t-test based on the difference between the pre- and post-scores of the creative problem solving measurement test, significant statistical test results were shown in 'idea adaptation', 'imaging', 'analogy', 'idea production' and 'elaboration' sub-factors except 'attention task' sub-factor (p < .05). Third, in the paired t-test based on the difference between the pre- and post-scores of the STEAM attitude test, significant statistical test results were shown in 'interest', 'communication', 'self-concept', 'self-efficacy' and 'science and engineering career choice' sub-factors except 'consideration' and 'usefulness / value recognition' sub-factors (p < .05). Fourth, in the STEAM satisfaction test conducted after class application, the average values of sub-factors were 3.16~3.90. The results indicated that students' understanding and interest in the science subject improved significantly through the big data-based STEAM program using the WWT.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.6
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• pp.309-314
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• 2017

In general, there are many difficulties in collecting, storing, and analyzing SNS (social network service) data, since those data have big data characteristics, which occurs very fast with the mixture form of structured and unstructured data. In this paper, we propose a new data visualization framework that works on Apache Storm, and it can be useful for real-time and dynamic analysis of SNS data. Apache Storm is a representative big data software platform that processes and analyzes real-time streaming data in the distributed environment. Using Storm, in this paper we collect and aggregate the real-time Twitter data and dynamically visualize the aggregated results through the tag cloud. In addition to Storm-based collection and aggregation functionalities, we also design and implement a Web interface that a user gives his/her interesting keywords and confirms the visualization result of tag cloud related to the given keywords. We finally empirically show that this study makes users be able to intuitively figure out the change of the interested subject on SNS data and the visualized results be applied to many other services such as thematic trend analysis, product recommendation, and customer needs identification.