• Journal of Korea Game Society
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• v.22 no.1
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• pp.87-98
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• 2022

With the development of fourth industrial revolution-related technology and increased demands for non-face-to-face services, VR games attract attention. This study collected VR game review data from an online game platform STEAM and analyzed chronical trends using text mining and word cloud analysis. According to the results, experience and perceived cost were major trends from 2016 to 2017, increased demands for FPS and rhythm games were from 2018 to 2019, and story and immersion were from 2020 to 2021. It aims to contribute to expanding the base of VR games by identifying the keywords VR users take interest in by period.

• Journal of Satellite, Information and Communications
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• v.12 no.1
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• pp.137-142
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• 2017

In this paper, we propose proposed mobile wagering platform technology, where the buying lottery contents registered in secure storage through lottery technology can be used as a common experience in smartphones, smart pads, and PCs, etc. Currently, many people are producing and consuming various types of contents in bulk, and it is expected that real-time contents and old contents coexist as IoT(Internet of Things) technology is commonly deployed in the future. Therefore, we need to develop a differentiated service that can compete with global services in lottery contents authoring and collaboration systems to create new markets. Accordingly, we implemented an wagering service platform to occupy cloud markets with high quality lottery contents produced through collaboration.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.3
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• pp.491-500
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• 2013

With the advent of big data and increased costs of SSD(HDD), domestic and foreign Internet cafes and organizations have adopted NDS(No Disk System) solution recently. NDS is a storage virtualization solution based on a kind of cloud computing. It manages Operating System and applications in the central server, which were originally managed by individual computers. This research will illustrate the way to analyze user's behaviors under NDS circumstance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.3
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• pp.585-594
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• 2015

As the storage market has been expanded due to growing data size, the research on various kinds of storages such as cloud, USB, and external HDD(Hard Disk Drive) has been conducted in digital forensic aspects. NAS(Network-Attached Storage) can store the data over one TB(Tera Byte) and it is well used for private storage as well as for enterprise, but there is almost no research on NAS. This paper selects three NAS products that has the highest market share in domestic and foreign market, and suggests the process and method for data acquisition in live NAS System.

• Journal of Internet Computing and Services
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• v.16 no.3
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• pp.79-93
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• 2015

With the rapid development of information technology, the ways of usage have changed drastically. The ways and efficiency of traditional service application to data processing already could not satisfy the requirements of modern users. Nowadays, users have already understood the importance of data. Therefore, the processing and saving of big data have become the main research of the Internet service company. In China, with the rise and explosion of 115 Cloud leads to other technology companies have began to join the battle of cloud services market. Although currently Chinese cloud services are still mainly dominated by cloud storage service, the series of service contents based on cloud storage service have been affirmed by users, and users willing to try these new ways of services. Thus, how to let users to keep using cloud services has become a topic that worth for exploring and researching. The academia often uses the TAM model with statistical analysis to analyze and check the attitude of users in using the system. However, the basic TAM model obviously already could not satisfy the increasing scale of system. Therefore, the appropriate expansion and adjustment to the TAM model (i. e. TAM2 or TAM3) are very necessary. This study has used the status of Chinese internet users and the related researches in other areas in order to expand and improve the TAM model by adding the brand influence, hardware environment and external environments to fulfill the purpose of this study. Based on the research model, the questionnaires were developed and online survey was conducted targeting the cloud services users of four Chinese main cities. Data were obtained from 210 respondents were used for analysis to validate the research model. The analysis results show that the external factors which are service contents, and brand influence have a positive influence to perceived usefulness and perceived ease of use. However, the external factor hardware environment only has a positive influence to the factor of perceived ease of use. Furthermore, the perceived security factor that is influenced by brand influence has a positive influence persistent intention to use. Persistent intention to use also was influenced by the perceived usefulness and persistent intention to use was influenced by the perceived ease of use. Finally, this research analyzed external variables' attributes using other perspective and tried to explain the attributes. It presents Chinese cloud service users are more interested in fundamental cloud services than extended services. In private cloud services, both of increased user size and cooperation among companies are important in the study. This study presents useful opinions for the purpose of strengthening attitude for private cloud service users can use this service persistently. Overall, it can be summarized by considering the all three external factors could make Chinese users keep using the personal could services. In addition, the results of this study can provide strong references to technology companies including cloud service provider, internet service provider, and smart phone service provider which are main clients are Chinese users.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

• Journal of Korean Society for Quality Management
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• v.41 no.3
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• pp.381-394
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• 2013

Purpose: Nowadays, Mobile Cloud Storage services are used widely. For sustainable use of this service, we need to determine what factors affect satisfaction. Therefore, the purpose of this study is to identify the factors that influence satisfaction. Methods: To analyze factors that influence satisfaction, this study sets the factors into three dimensions such as service quality, perceived risk, and individual characteristics and analyze the causal relationship between influence factors and satisfaction through Structural Equation Model. Results: The results of this study are as follows; among service quality, user interface and reliability influenced satisfaction, but adaptability did not have any influence. Perceived risk of illegal access had a negative influence on satisfaction, while perceived risk of privacy leakage did not have significant influence on satisfaction in perceived risk. At last, self-efficacy had a significant influence on satisfaction. Conclusion: We identified the influence factors that influence satisfaction. Our findings will be necessary for Mobile Cloud Storage service providers to strengthen their service.

• Journal of Information Technology Services
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• v.14 no.3
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• pp.197-215
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• 2015

Recently, many internet users are using cloud computing. Users can manage, store and share their data and information by using personal cloud storage. In this paper, we aim to figure out influencing factors on personal cloud storage selection. The causal relationship between factors were identified through a importance analysis by using AHP(Analytic Hierarchy Process). AHP is a structured technique for organizing and analyzing complex decisions, based on mathematics and psychology. Research model consists of upper factorsincluding system factor, service factor and user factor. 12 lower factors and 6 alternatives were also analyzed. Asa result, system factor of 3 upper factors was found as the most important factor. Purpose-coincidence, security andaccessibility were top 3 factors among lower factors. N drive showed top importance value. We also conducted ANOVAby classifying 4 groups according to gender, age, currently used cloud and cloud to use. The results of this researchcan be useful guidelines for cloud computing industry.

• Journal of Platform Technology
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• v.11 no.4
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• pp.71-79
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• 2023

In this paper, we analyze the characteristics of machine learning workloads and, based on them, propose a distributed in-memory caching technique to improve the performance of machine learning workloads. The core of machine learning workload is model training, and model training is a computationally intensive task. Performing machine learning workloads in a Kubernetes-based cloud environment in which the computing framework and storage are separated can effectively allocate resources, but delays can occur because IO must be performed through network communication. In this paper, we propose a distributed in-memory caching technique to improve the performance of machine learning workloads performed in such an environment. In particular, we propose a new method of precaching data required for machine learning workloads into the distributed in-memory cache by considering Kubflow pipelines, a Kubernetes-based machine learning pipeline management tool.

• Journal of Information Technology Applications and Management
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• v.30 no.6
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• pp.91-111
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• 2023

This study aims to identify the influencing factors of intention to use cloud services using the SEM-ANN two-step approach. In previous studies of SEM-ANN, SEM presented R² and ANN presented MSE(mean squared error), so analysis performance could not be compared. In this study, R² and MSE were calculated and presented by SEM and ANN, respectively. Then, analysis performance was compared and feature importances were compared by sensitivity analysis. As a result, the ANN default model improved R² by 2.87 compared to the PLS model, showing a small Cohen's effect size. The ANN optimization model improved R² by 7.86 compared to the PLS model, showing a medium Cohen effect size. In normalized feature importances, the order of importances was the same for PLS and ANN. The contribution of this study, which links structural equation modeling to artificial intelligence, is that it verified the effect of improving the explanatory power of the research model while maintaining the order of importance of independent variables.