• Journal of Intelligence and Information Systems
• /
• v.21 no.1
• /
• pp.143-159
• /
• 2015

Predicting IT trends has been a long and important subject for information systems research. IT trend prediction makes it possible to acknowledge emerging eras of innovation and allocate budgets to prepare against rapidly changing technological trends. Towards the end of each year, various domestic and global organizations predict and announce IT trends for the following year. For example, Gartner Predicts 10 top IT trend during the next year, and these predictions affect IT and industry leaders and organization's basic assumptions about technology and the future of IT, but the accuracy of these reports are difficult to verify. Social media data can be useful tool to verify the accuracy. As social media services have gained in popularity, it is used in a variety of ways, from posting about personal daily life to keeping up to date with news and trends. In the recent years, rates of social media activity in Korea have reached unprecedented levels. Hundreds of millions of users now participate in online social networks and communicate with colleague and friends their opinions and thoughts. In particular, Twitter is currently the major micro blog service, it has an important function named 'tweets' which is to report their current thoughts and actions, comments on news and engage in discussions. For an analysis on IT trends, we chose Tweet data because not only it produces massive unstructured textual data in real time but also it serves as an influential channel for opinion leading on technology. Previous studies found that the tweet data provides useful information and detects the trend of society effectively, these studies also identifies that Twitter can track the issue faster than the other media, newspapers. Therefore, this study investigates how frequently the predicted IT trends for the following year announced by public organizations are mentioned on social network services like Twitter. IT trend predictions for 2013, announced near the end of 2012 from two domestic organizations, the National IT Industry Promotion Agency (NIPA) and the National Information Society Agency (NIA), were used as a basis for this research. The present study analyzes the Twitter data generated from Seoul (Korea) compared with the predictions of the two organizations to analyze the differences. Thus, Twitter data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. To overcome these challenges, we used SAS IRS (Information Retrieval Studio) developed by SAS to capture the trend in real-time processing big stream datasets of Twitter. The system offers a framework for crawling, normalizing, analyzing, indexing and searching tweet data. As a result, we have crawled the entire Twitter sphere in Seoul area and obtained 21,589 tweets in 2013 to review how frequently the IT trend topics announced by the two organizations were mentioned by the people in Seoul. The results shows that most IT trend predicted by NIPA and NIA were all frequently mentioned in Twitter except some topics such as 'new types of security threat', 'green IT', 'next generation semiconductor' since these topics non generalized compound words so they can be mentioned in Twitter with other words. To answer whether the IT trend tweets from Korea is related to the following year's IT trends in real world, we compared Twitter's trending topics with those in Nara Market, Korea's online e-Procurement system which is a nationwide web-based procurement system, dealing with whole procurement process of all public organizations in Korea. The correlation analysis show that Tweet frequencies on IT trending topics predicted by NIPA and NIA are significantly correlated with frequencies on IT topics mentioned in project announcements by Nara market in 2012 and 2013. The main contribution of our research can be found in the following aspects: i) the IT topic predictions announced by NIPA and NIA can provide an effective guideline to IT professionals and researchers in Korea who are looking for verified IT topic trends in the following topic, ii) researchers can use Twitter to get some useful ideas to detect and predict dynamic trends of technological and social issues.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.23-45
• /
• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• The Journal of the Korea Contents Association
• /
• v.20 no.11
• /
• pp.249-257
• /
• 2020

The study aims to explore whether the Supplier's social medial activities and the consumer's social media activities mediate between the characteristics of purchasing cosmetics and the intention of purchasing. To answer this question, a total of 321 consumers participated in the study, and a self-report type of questionnaires were utilized to collect the data. For the data analysis, descriptive statistical analysis and structural equation modeling(SEM) were applied. The results from the study are as follows. First, the characteristics of purchasing cosmetics did not affect the intention of purchasing cosmetics. Second, both the Supplier's and the consumer's social media activities positively predicted the intention of purchasing, among which the consumer's activities were ascertained to make more influential impact on the intention of purchasing cosmetics. Third, both Supplier's and consumer's social media activities completely mediated between the characteristics of purchasing cosmetics and the intention of purchasing. More discussions are suggested for marketing strategies in the study.

• Journal of Distribution Science
• /
• v.14 no.11
• /
• pp.117-128
• /
• 2016

Purpose - In order to collect information needed for the establishment of more effective marketing strategies of on-line purchasing agent services targeting Chinese consumers, the study investigated the relationship among Chinese selection criteria. They included fashion products, use of information source, and satisfaction with on-line purchasing agent services. The study also identified the differences in the Chinese selection criteria of fashion products, use of information source, and the satisfaction level with on-line purchasing agent services according to their age and gender. Research design, data, and methodology - The study was implemented through a normative-descriptive survey method using a self-administered questionnaire. Data were collected from February 9 to 28, 2016, and analyzed by factor analysis, ANOVA and Duncan test, t-test, and multiple regression analysis. Results - Differences were found in selection criteria of fashion products and use of information sources among groups. Thirty's age group was concerned about price/brand more than the twenty's were. Twenty's were concerned about practicality/quality of the products more than the thirty's. Hallyu/broadcasting was used by men more than by women as an information source of Korean fashion. SNS/WOM(word of mouth) was used more by women than by man. Twenty's showed lower level of satisfaction with customer services/credibility than other factors. The thirty's showed lower level of satisfaction with informational role of the service than other factors. Those who utilize each type of fashion information source more showed higher satisfaction level with on-line purchasing agent service of Korean fashion products.. In general, according to the selection criteria and use of information, there were differences in satisfaction with on-line purchasing agent service of Korean fashion products. Conclusions - Considering the findings of the study, as well as age, gender, selection criteria and use of information source, Chinese consumers could be used as a criteria of market segmentation for on-line purchasing agent services of Korean fashion products. The results manifested that there is a need to differentiate marketing strategies according to the satisfaction levels with each satisfaction factors of on-line purchasing agent service of Korean fashion products.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.6
• /
• pp.237-249
• /
• 2017

This research aims to gain a better understanding of college students' thoughts on what factors make them happy and contribute to enhancing their happiness. We focused on the relationship between their self-assessed happiness and various factors affecting happiness, such as their socio-economic status, relationships with others, future orientation, and daily activities. Survey data were collected from October, 2014 to December, 2014 at a South Korean University. The final total number of respondents was 474 from 500 distributed questionnaires, after excluding 26 responses with missing values and unanswered items. The response was comprised of 247 male students, and 227 female students, and of 268 freshmen, 145 sophomores, 35 juniors, and 26 senior students. Factors that were statistically significant were gender, year, average cost of leisure, appearance satisfaction, conversation hour with parents, having girlfriend/boyfriend, sexual experience, number of friends, satisfaction with major, Grade Point Average (GPA), studying hours, time for self-improvement, reading hours, use of smart phone hours, number of daily meals, exercise hour, schedule management and future goal setting. This research was conducted utilizing only data from one university and so it may not be appropriate to generalize the results. Moreover, some of the variables are not in line with previous studies on happiness. Some other mediating variables may exist. Therefore, following research should be conducted.

• Journal of Korea Society of Industrial Information Systems
• /
• v.17 no.1
• /
• pp.47-62
• /
• 2012

To propagate clinical disease management service, there should be built a ecosystem where service developers, service providers, device suppliers closely cooperate for u-Health platform. However, most u-Health platform is difficult to build an effective ecosystem due to the lack of secure and effective PHR(Personal Health Record) management, the lack of personalized and intelligent service, difficulties of N-screen service. To solve these problems we suggest the CDMP(Chronic Disease Management Platform) architecture. The CDMP is a software platform that provides the core functions to develop the chronic disease management services and performs a hub function for the link and integration rbetween various services and systems. CDMP is SOA based platform that enables a provision of reusability, expansibility and it provides open API where everybody can share information, contents and services easily. CDMP supports the multi platform system foN-screen service and the self management functions via SNS. In this paper, we design and implement the CDMP including PHR service based on hybrid data model for privacy preservation. Experiment results prove the effectiveness of hybrid model-based PHR service.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.1
• /
• pp.95-110
• /
• 2014

The purpose of this research is to further investigate the influence of partnership between with the mediator effect of the social business on the outcome of SCM. IT technology fusion electronic tags, mobile phone, such as cloud computing is also activated in supply chain management of recently, business is faster, if social business is applied here that are smarter, customers or suppliers, there may be communication directly and to further improve the relationship partnership. 150 questionnaires were sent to companies that have introduced SCM to their systems and are operating it. Among 150 questionnaires, 127 collected data were analyzed excluding incomplete 23 data. Statistical methods used in this study were frequency analysis, factor analysis, reliability analysis, t-test, ANOVA, path analysis, Scheffe test and Sobel test with Amos 18.0. and SPSS 21.0. The analytical results are as follows. First, the more the reliability, information share, continuous transaction, effects on the social business are getting higher, the interdependence has little impact on it. Second, the impact on the outcome of SCM, partnerships between companies, showed a significant influence the reliability, the share of information, the continuous transaction, but the interdependence was analysed as an uninfluential factor. Third, the social business is analyses to have a mediator effect in relationship between the partnership and the outcome of SCM.

• The Journal of Industrial Distribution & Business
• /
• v.9 no.7
• /
• pp.61-70
• /
• 2018

Purpose - The purpose of this research is reflected on the rapid development of online tourism industries. The study was to establish the strategy for Korean tourism enterprises to develop tourist commodities suitable for Chinese tourists and attract them to visit Korea by the empirical analysis of the relation between repurchase intention of tourists and its premise variables (e-service quality, perceived value and satisfaction). Research design, data, and methodology - This research carried out a questionnaire survey on Chinese tourists who visited Korea with experience of using the online travel agency web sites. A total 398 answers were recovered, 41 of them were excluded due to the dishonest answers and 357 of them were finally analyzed. The data was analyzed with IBM SPSS AMOS 22.0. Results - The research results show that in the online travel agency web site e-service quality, convenience, interactivity, information validity, credibility had a positive impacts on perceived value and satisfaction. The perceived value of online travel agency website users has positive impart on satisfaction and repurchase intention. Satisfaction of online travel agency web site users have positive impacts on repurchase intention. But safety has no impact on perceived value while positive impacts on satisfaction was affected. Conclusions - First, in the online travel agency web site e-service quality, safety has no impact on perceived value while it was shown to have positive impacts on satisfaction because the users of online travel agency web sites believe that the protection of personal information, the defense of cracker and the safeguard of payment security are the basic premises of website operation. Although safety does not have impacts on perceived value, users benefits will suffer damage when hacker intrusion and other accidents occur so that online travel agency web sites should not ignore the security concerns. Second, credibility is a major concern for online travel agency web site users. At this time, it is necessary for the web site to establish a system to display both the commodity information and the using experience published on the user's SNS, thus improving the credibility of the website information.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.40 no.8
• /
• pp.1551-1559
• /
• 2015

Recently, the performance of smart devices is almost similar to that of the existing PCs, thus the users of smart devices can perform similar works such as messengers, SNSs(Social Network Services), smart banking, etc. originally performed in PC environment using smart devices. Although the development of smart devices has led to positive impacts, it has caused negative changes such as an increase in security threat aimed at mobile environment. Specifically, the threats of mobile devices, such as leaking private information, generating unfair billing and performing DDoS(Distributed Denial of Service) attacks has continuously increased. Over 80% of the mobile devices use android platform, thus, the number of damage caused by mobile malware in android platform is also increasing. In this paper, we propose android based malware detection mechanism using time-series analysis, which is one of statistical-based detection methods.We use auto-regressive moving-average model which is extracting accurate predictive values based on existing data among time-series model. We also use fast and exact malware detection method by extracting possible malware data through Z-Score. We validate the proposed methods through the experiment results.

• The Journal of the Korea Contents Association
• /
• v.21 no.10
• /
• pp.603-612
• /
• 2021

The purpose of this study is to allow beginner level English learners to experience the English speaking task using pictures, and to analyze the meanings of the experience using a phenomenological research method. As research participants, 10 freshmen majoring in Power Generation Facilities at Korean Polytechnic University in Gangwon-do were selected. Face-to-face interviews and SNS were used for data collection, and Colaizzi's research method was adopted for data analysis. As a result of the analysis, 9 themes, 4 theme clusters, and 2 categories were derived. The results are as follows. First, the participants were able to find hope that they could speak English at their own level through the English speaking task using pictures. Second, they stated that the effect of the visual medium of painting increased concentration and curiosity and lowered anxiety. Third, it was recognized that self-confidence, a speaker like a native speaker, and quickness of speaking improved due to familiarity with speaking English. Fourth, the biggest difficulty in the English speaking task was vocabulary. So, they felt the limitation in explaining the picture, and they were having a lot of trouble in translating Korean words into English words. Finally, through the results of this study, the effect of the medium of picture was confirmed, and necessary future studies were suggested.