• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.9
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• pp.1655-1665
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• 2017

Recently data centers are being constructed actively by many cloud service providers, enterprises, research institutes, etc. Generally, they are built on tree topology using ECMP data forwarding scheme for load balancing. In this paper, we examine data center network topologies like tree topology and fat-tree topology, and load balancing technologies like MLAG and ECMP. Then, we propose a scheduling algorithm to efficiently transmit particular files stored on the hosts in the data center to the destination node outside the data center, where fat-tree topology and OpenFlow protocol between infrastructure layer and control layer are used. We run performance analysis by numerical method, and compare the analysis results with those of ECMP. Through the performance comparison, we show the outperformance of the proposed algorithm in terms of throughput and file transfer completion time.

• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1601-1614
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• 2008

The number of web service users has been increased rapidly as existing services are changed into the web-based internet applications. Therefore, it is necessary for us to use web log pre-processing technique to detect attacks on diverse web service transactions and it is also possible to extract web mining information. However, existing mechanisms did not provide efficient pre-processing procedures for a huge volume of web log data. In this paper, we proposed both a field based parsing and a high-speed log indexing mechanism based on the suggested B-tree Index Vector structure for performance enhancement. In experiments, the proposed mechanism provides an efficient web log pre-processing and search functions with a session classification. Therefore it is useful to enhance web attack detection function.

• Information Systems Review
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• v.22 no.4
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• pp.41-58
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• 2020

This study addresses the issue of which factors make domestic works have turnover intention. To pursue this research issue, we utilized a public database "2017 Occupational Migration Path Survey", administerd by Korea Employment Information Service (KEIS). Decision tree method was applied to extract crucial factors influencing workers' turnover intention. They include 'the degree of matching the level of education with the level of work', 'the possibility of individual development', 'the job-related education and training', 'the promotion system', 'wage and income', 'social reputation for work' and 'the stability of employment'.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.595-601
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• 2020

During the safety inspection of nitrocellulose-made explosive containers stored for more than 10 years, cracks were found in the containers. Therefore, a failure cause analysis test was performed. First, the cause of failure through the failure tree analysis was conducted to select the factors that influenced failure. The changes in the properties of the container caused by the acceleration of the reaction were found to be the cause of the failure by confirming the influence on the environment and internal/external factors that may occur during storage. To confirm this, environmental tests, such as thermal shock test and vacuum thermal stability test, were performed using a naturally aged container to analyze the cause of failure, and an accelerated aging test was performed to reproduce the failure. Through this, the chemical reaction was accelerated by heat and charge, as in the result of the fault tree analysis, and it was confirmed that the physical properties of the container were changed. In addition, the service life of the container was estimated using the Arrhenius model for the storage life due to thermal aging.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.4
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• pp.981-987
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• 2017

Proliferation of cloud computing services brings about reduction of the maintenance and management costs by allowing data to be outsourced to a dedicated third-party remote storage. At the same time, the majority of storage service providers have adopted a data deduplication technique for efficient utilization of storage resources. When a hash tree is employed for duplicate identification as part of deduplication process, size information of the attested data and partial information about the tree can be deduced from eavesdropping. To mitigate such side channels, in this paper, a new duplicate identification method is presented by exploiting a multi-set hash function.

• Journal of the Korean Wood Science and Technology
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• v.45 no.6
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• pp.682-688
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• 2017

Fresh japanese anise (Illicium anisatum L.) tree leaves were collected and ground after drying. The essential oils of the leaves were analyzed by gas chromatography-mass spectrometry (GC-MS) using headspace (HS) and solid phase-microextra (SPME) methods. Volatile components of the leaves were identified 21 and 65 components in HS and SPME, respectively. The main components of the essential oils obtained by HS method were eucalyptol (36.7%), (+)-sabinene (15.61%), ${\delta}$-3-carene (6.87%), ${\alpha}$-pinene (6.07%), ${\gamma}$-terpinen (5.72%), ${\alpha}$-limonene (5.26%), ${\beta}$-myrcene (4.13%), ${\alpha}$-terpinene (4.04%) and ${\beta}$-pinene (3.73%). The other components were less than 3.5%. SPME method also showed that eucalyptol (17.88%) was main. The other were 5-allyl-1-methoxy-2 (13.29%), caryophyllene (6.09%), (+)-sabinene (5.60%), ${\alpha}$-ocimene (4.89%) and ${\beta}$-myrcene (3.73%), and the rest were less amounts than 3.5%. This work indicated that many more volatile components were isolated, comparing to the previous literature data and that SPME method was much more effective than HS method in the analysis of the volatile components.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.21-41
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• 2019

Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.

• Journal of the FoodService Safety
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• v.2 no.1
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• pp.10-21
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• 2021

According to the analysis which investigated visitors of HACCP system certified stores and non-visitors of such stores on the awareness of HACCP for livestock products, 77.1% (246 pollees) heard of HACCP certification for livestock products, 67.1% (214 pollees) had seen the HACCP certification mark for livestock products, 62.1% (198 pollees) heard of HACCP certification for livestock products in meet retail shops, and 51.4% (164 pollees) were not aware of the recent TV · subway advertisements regarding HACCP certification for livestock products. For every questionnaire on the awareness of HACCP for livestock products, visitors of HACCP system certified stores showed significantly higher response rate than nonvisitors (p<0.01, p<0.001). The majority of pollees (74.9%, 239 pollees) replied that the word HACCP for livestock products brings up the image of safe livestock products, and 37.0% answered that the term HACCP defines 'Hazard analysis critical control point'. Regarding the questions on HACCP system for livestock products, 38.6% showed that they were most curious in terms of the benefits of such system. The demand analysis on HACCP for livestock products for consumer was also conducted. In the analysis, the demand for support of the policy (4.06 points) was higher than demand for education · public promotion of HACCP (4.03 points) and demand for related application (3.90 points).

• Journal of the Korea Society of Computer and Information
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• v.25 no.2
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• pp.213-219
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• 2020

In this paper, we propose a classification analysis method for diagnosing and predicting problematic smartphone use in order to provide policy data on problematic smartphone use, which is getting worse year after year. Attempts have been made to identify key variables that affect the study. For this purpose, the classification rates of Decision Tree, Random Forest, and Support Vector Machine among machine learning analysis methods, which are artificial intelligence methods, were compared. The data were from 25,465 people who responded to the '2018 Problematic Smartphone Use Survey' provided by the Korea Information Society Agency and analyzed using the R statistical package (ver. 3.6.2). As a result, the three classification techniques showed similar classification rates, and there was no problem of overfitting the model. The classification rate of the Support Vector Machine was the highest among the three classification methods, followed by Decision Tree and Random Forest. The top three variables affecting the classification rate among smartphone use types were Life Service type, Information Seeking type, and Leisure Activity Seeking type.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2015.10a
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• pp.256-257
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• 2015

The trend of globalization and the development of the communication-Information technology not only complexified the supply chain, but also, led to the needs of the high quality of logistics service for customers. I t defines risks that can occur in truck transport under unexpected situation with Fault Tree Analysis(FTA) and calculates failure rate concerning relationship between each risks. Based on the 4 kinds of middle failure events that defined in FTA, Reliability function which is regarded about risks sequentiality and time flow is resulted in. I t is meaningful that it calculates reliability of logistics and transportation system with engineering methodology.