• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.53-59
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• 2016

Recently, huge stream data have been generated in real time from various applications such as wireless sensor networks, Internet of Things services, and social network services. For this reason, to develop an efficient method have become one of significant issues in order to discover useful information from such data by processing and analyzing them and employing the information for better decision making. Since stream data are generated continuously and rapidly, there is a need to deal with them through the minimum access. In addition, an appropriate method is required to analyze stream data in resource limited environments where fast processing with low power consumption is necessary. To address this issue, the sliding window model has been proposed and researched. Meanwhile, one of data mining techniques for finding meaningful information from huge data, pattern mining extracts such information in pattern forms. Frequency-based traditional pattern mining can process only binary databases and treats items in the databases with the same importance. As a result, frequent pattern mining has a disadvantage that cannot reflect characteristics of real databases although it has played an essential role in the data mining field. From this aspect, high utility pattern mining has suggested for discovering more meaningful information from non-binary databases with the consideration of the characteristics and relative importance of items. General high utility pattern mining methods for static databases, however, are not suitable for handling stream data. To address this issue, sliding window based high utility pattern mining has been proposed for finding significant information from stream data in resource limited environments by considering their characteristics and processing them efficiently. In this paper, we conduct various experiments with datasets for performance evaluation of sliding window based high utility pattern mining algorithms and analyze experimental results, through which we study their characteristics and direction of improvement.

• The Korean Journal of Vision Science
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• v.20 no.4
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• pp.403-411
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• 2018

Purpose : The purpose of this study is to analyze how the certain efforts of the optical shops affect on opticians' job satisfaction and organizational commitment, and to analyze how opticians' job satisfaction and organizational commitment affect on the customer orientation and to suggest the method of maintaining competitiveness. Methods : Two hundred opticians took participations on the surveys via the Internet survey method and social network system (SNS), and SPSS 18.0 statistics program was used for data analysis; frequency analysis, T-test, factor analysis, reliability analysis, and multiple linear regression analysis were conducted. Results : It is analyzed the differences on the job satisfaction and organizational commitment in accordance with type of optical shops, conducting 5 working days/week and flexible time. As a result, higher job satisfaction is presented with 5 working days/week. Relationships with co-workers statistically affect on job satisfaction positively and emotional labor and work overload statistically affect on job satisfaction negatively. In addition, relationships with co-workers and reward statistically affect on organizational commitment positively and emotional labor and work overload statistically affect on organizational commitment negatively. And opticians' job satisfaction and organizational commitment statistically affect on customer satisfaction positively. Conclusion : It is necessary for the optical shops to make an effort for their opticians to improve the job satisfaction and organizational commitment. And to do so, it is needed to form trusting and respecting relationships with co-workers or superiors. In addition, it is necessary to have continuous communication and education for opticians' self-management. Also, it is needed to establish an effective reward system.

• Journal of Internet Computing and Services
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• v.20 no.3
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• pp.43-59
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• 2019

Today, with the spread of smartphones and the development of social networking services, structured and unstructured big data have stored exponentially. If we analyze them well, we will get useful information to be able to predict data for the future. Large amounts of data need to be collected first in order to analyze big data. The web is repository where these data are most stored. However, because the data size is large, there are also many data that have information that is not needed as much as there are data that have useful information. This has made it important to collect data efficiently, where data with unnecessary information is filtered and only collected data with useful information. Web crawlers cannot download all pages due to some constraints such as network bandwidth, operational time, and data storage. This is why we should avoid visiting many pages that are not relevant to what we want and download only important pages as soon as possible. This paper seeks to help resolve the above issues. First, We introduce basic web-crawling algorithms. For each algorithm, the time-complexity and pros and cons are described, and compared and analyzed. Next, we introduce the state-of-the-art web crawling algorithms that have improved the shortcomings of the basic web crawling algorithms. In addition, recent research trends show that the web crawling algorithms with special purposes such as collecting sentiment words are actively studied. We will one of the introduce Sentiment-aware web crawling techniques that is a proactive web crawling technique as a study of web crawling algorithms with special purpose. The result showed that the larger the data are, the higher the performance is and the more space is saved.

• Science of Emotion and Sensibility
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• v.22 no.2
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• pp.59-69
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• 2019

The present study explored consumer's perception and preference on providing information of fashion products by using QR code and suggested the possibility for consumer-to-consumer and consumer-to-company connection. A survey was conducted on males and females in their 20s-a population among whom the rate of smart phone penetration is higher than in any other age group and who tend to exchange information online. The results showed that consumers are dissatisfied with the amount of information, terms of instructions, and ambiguous washing symbols currently provided. Therefore, the study identified the need for better methods of providing information and found that QR code, which is able to deliver high-quality information on fashion products, can be an efficient alternative. Moreover, respondents felt the need for detailed washing instructions, information on handling, and functionality of material on high-involvement fashion products such as outdoor, padding, suit, and underwear worn next to the skin. They also desire styling tips or purchasing information such as SNS OOTD (Outfit Of The Day) utilizing the product, other products that may go well with the one purchased, and similar products on casual wear and coat used on a daily basis. Therefore, QR code used as a link to information web pages or a social network can help consumers to satisfy information needs and to use the products effectively.

• Journal of Mushroom
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• v.17 no.3
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• pp.167-170
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• 2019

Cultivation of brown mushrooms, rather than that of white variants is preferred by Korean mushroom farmers, as the former are resistant to diseases. However, brown mushrooms were cultivated only in selective eco-friendly agricultural farms due to lack of consumer awareness. After providing information about brown mushrooms to respondents through a 1-minute video clip, a survey was conducted on social network service (SNS) to assess recognition and preference for brown mushrooms. A food evaluation was then conducted among 200 people randomly selected from the survey respondents. Most respondents (83%) had not encountered brown button mushrooms previously, and 98% of the respondents were willing to buy these mushrooms because they were "curious about its taste" (44%). In the food evaluation, 32% of the respondents found the brown button mushrooms to be delicious, 28% reported a good flavor, and 31% described a good texture. In addition, we confirmed that 95% of respondents were interested in purchasing brown mushrooms after sampling. Therefore, in the present study, we evaluated public perception, preference, and taste of brown button mushrooms, and confirmed that availability of information on nutrition and benefits s of mushroom consumption could induce consumers to buy brown button mushrooms.

• Journal of Digital Convergence
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• v.19 no.2
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• pp.231-238
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• 2021

SNS data can catch the user's thoughts and actions. And the trend of the search portal is a representative service that can observe the interests of users and their changes. In this paper, the relationship was analyzed by comparing statistics on narcotics incidents and the degree of exposure to narcotics related words in tweets of SNS and in the trends of search portal. It was confirmed that the trend of SNS and search portal trends was the same in the statistics of the prosecution office with a certain time difference.In addition, cluster analysis was performed to understand the meaning of tweets in which narcotics related words were mentioned. In the 50,000 tweets collected in January 2020, it was possible to find meaning related to the sale of actual drugs. Therefore, through SNS monitoring alone it is possible to monitor narcotics-related incidents and to find specific sales or purchase-related information, and this can be used in the investigation process. In the future, it is expected that crime monitoring and prediction systems can be proposed as related crime analysis may be possible not only with text but also images.

• The Southeast Asian review
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• v.28 no.1
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• pp.173-218
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• 2018

This study focuses on the healthcare sector in Vietnam which is promoting universal health insurance for the achievement of Universal Health Coverage (UHC) under Sustainable Development Goals (SDGs). The purpose of this study is to examine the characteristics of the reform process of the health care system and the law on health insurance through the historical and cultural contexts and its implications from the perspective of development. Based on the three dimensions of UHC - extension of protection for population, provision of various medical services, and financial protection, the current status of the Vietnam healthcare sector is summarized respectively as follows. First, according to the revised Health Insurance law which came into effect in 2015, the mandatory health insurance premiums are calculated based on household units. Second, there is a medical network that can provide preventive and healthcare services centered on primary health care facilities, for example commune health stations (trạm y $t{\hat{e}}$ $X{\tilde{a}}$). Third, out-of-pocket expenditure is still a large proportion although public spending has increased and private spending has decreased since the enforcement of the health insurance law and various schemes. Vietnam is currently striving towards a universal health care system. The development of institutions and systems should be designed in a way that is appropriate for the members of the society rather than efficiency. This article findings shed light on the role of social values, family culture, and informal institutions.

• Knowledge Management Research
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• v.22 no.2
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• pp.19-32
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• 2021

Bitcoin, a representative cryptocurrency, is receiving a lot of attention around the world, and the price of Bitcoin shows high volatility. High volatility is a risk factor for investors and causes social problems caused by reckless investment. Since the price of Bitcoin responds quickly to changes in the world environment, we propose to predict the price volatility of Bitcoin by utilizing news information that provides a variety of information in real-time. In other words, positive news stimulates investor sentiment and negative news weakens investor sentiment. Therefore, in this study, sentiment information of news and deep learning were applied to predict the change in Bitcoin yield. A single predictive model of logit, artificial neural network, SVM, and LSTM was built, and an integrated model was proposed as a method to improve predictive performance. As a result of comparing the performance of the prediction model built on the historical price information and the prediction model reflecting the sentiment information of the news, it was found that the integrated model based on the sentiment information of the news was the best. This study will be able to prevent reckless investment and provide useful information to investors to make wise investments through a predictive model.

• Korean Journal of Cognitive Science
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• v.33 no.1
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• pp.23-50
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• 2022

Source memory is a cognitive process that combines the representation of the origin of the episodic experience with an item. By studying this daily process, researchers have made fundamental discoveries that make up the foundation of brain and behavior research, such as executive function and binding. In this paper, we review and conduct a bibliometric analysis on source memory papers published from 1989 to 2020. This review is based on keyword co-occurrence networks and author citation networks, providing an in-depth overview of the development of source memory research and future directions. This bibliometric analysis discovers a change in the research trends: while research prior to 2010 focused on individuality of source memory as a cognitive function, more recent papers focus more on the implication of source memory as it pertains to connectivity between disparate brain regions and to social neuroscience. Keyword network analysis shows that aging and executive function are continued topics of interest, although frameworks in which they are viewed have shifted to include developmental psychology and meta memory. The use of theories and models provided by source memory research seem essential for the future development of cognitive enhancement tools within and outside of the field of Psychology.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.5
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• pp.13-24
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• 2022

SNS has become a very close service to our daily life. As marketing is done through SNS, places often called hot places are created, and users are flocking to these places. However, it is often crowded with a large number of people in a short period of time, resulting in a negative experience for both visitors and service providers. In order to improve this problem, it is necessary to recognize the congestion level, but the method to determine the congestion level in a specific area at an individual level is very limited. Therefore, in this study, we tried to propose a system that can identify the congestion level information and the characteristics of visitors to a specific tourist destination by using the data on the SNS. For this purpose, posting data uploaded by users and image analysis were used, and the performance of the proposed system was verified using the Naver DataLab system. As a result of comparative verification by selecting three places by type of tourist destination, the results calculated in this study and the congestion level provided by DataLab were found to be similar. In particular, this study is meaningful in that it provides a degree of congestion based on real data of users that is not dependent on a specific company or service.