• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.8
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• pp.1055-1060
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• 2018

In the wake of the 4th Industrial Revolution, the problem of career education in schools has become a big issue. While various studies are being conducted on services or technologies to effectively handle artificial intelligence and big data, in the field of education, data on students is simply processed. Therefore, in this paper, we are going to design and present career prediction programs for students using artificial intelligence and big data. Using observational data from students at the institute, the decision tree is constructed with the C4.5 algorithm known to be most intelligent and effective in the decision tree and is used to predict students' path of hope. As a result, the coefficient of kappa exceeded 0.7 and showed a fairly low average error of 0.1 degrees. As shown in this study, a number of studies and data will be deployed to help guide students in their consultation and to provide them with classroom attitudes and directions.

• Informatization Policy
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• v.25 no.4
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• pp.108-120
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• 2018

With the rise of the Fourth Industrial Revolution and I-Korea 4.0, both of which pursue strategies for industrial innovation and for the solution to social problems, the real estate industry needs to change in order to make effective use of available space in smart environments. The implementation of smart spaces is a promising solution for this. The smart space is defined as a good use of space, whether it be a home, office, or retail store, within a smart environment. To enhance the use of smart spaces, efficient decision-making and well-timed and accurate interaction are required. This paper proposes a smart space based on platform which takes advantage of emerging technologies for the efficient storage, processing, analysis, and utilization of big data. The platform is composed of six layers - collection, transfer, storage, service, application, and management - and offers three service frameworks: activity-based, market-based, and policy-based. Based on these smart space services, decision-makers, consumers, clients, and social network participants can make better decisions, respond more quickly, exhibit greater innovation, and develop stronger competitive advantages.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.93-110
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• 2015

The recommender system is a system which recommends products to the customers who are likely to be interested in. Based on automated information filtering technology, various recommender systems have been developed. Collaborative filtering (CF), one of the most successful recommendation algorithms, has been applied in a number of different domains such as recommending Web pages, books, movies, music and products. But, it has been known that CF has a critical shortcoming. CF finds neighbors whose preferences are like those of the target customer and recommends products those customers have most liked. Thus, CF works properly only when there's a sufficient number of ratings on common product from customers. When there's a shortage of customer ratings, CF makes the formation of a neighborhood inaccurate, thereby resulting in poor recommendations. To improve the performance of CF based recommender systems, most of the related studies have been focused on the development of novel algorithms under the assumption of using a single profile, which is created from user's rating information for items, purchase transactions, or Web access logs. With the advent of big data, companies got to collect more data and to use a variety of information with big size. So, many companies recognize it very importantly to utilize big data because it makes companies to improve their competitiveness and to create new value. In particular, on the rise is the issue of utilizing personal big data in the recommender system. It is why personal big data facilitate more accurate identification of the preferences or behaviors of users. The proposed recommendation methodology is as follows: First, multimodal user profiles are created from personal big data in order to grasp the preferences and behavior of users from various viewpoints. We derive five user profiles based on the personal information such as rating, site preference, demographic, Internet usage, and topic in text. Next, the similarity between users is calculated based on the profiles and then neighbors of users are found from the results. One of three ensemble approaches is applied to calculate the similarity. Each ensemble approach uses the similarity of combined profile, the average similarity of each profile, and the weighted average similarity of each profile, respectively. Finally, the products that people among the neighborhood prefer most to are recommended to the target users. For the experiments, we used the demographic data and a very large volume of Web log transaction for 5,000 panel users of a company that is specialized to analyzing ranks of Web sites. R and SAS E-miner was used to implement the proposed recommender system and to conduct the topic analysis using the keyword search, respectively. To evaluate the recommendation performance, we used 60% of data for training and 40% of data for test. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. A widely used combination metric called F1 metric that gives equal weight to both recall and precision was employed for our evaluation. As the results of evaluation, the proposed methodology achieved the significant improvement over the single profile based CF algorithm. In particular, the ensemble approach using weighted average similarity shows the highest performance. That is, the rate of improvement in F1 is 16.9 percent for the ensemble approach using weighted average similarity and 8.1 percent for the ensemble approach using average similarity of each profile. From these results, we conclude that the multimodal profile ensemble approach is a viable solution to the problems encountered when there's a shortage of customer ratings. This study has significance in suggesting what kind of information could we use to create profile in the environment of big data and how could we combine and utilize them effectively. However, our methodology should be further studied to consider for its real-world application. We need to compare the differences in recommendation accuracy by applying the proposed method to different recommendation algorithms and then to identify which combination of them would show the best performance.

• Asia pacific journal of information systems
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• v.23 no.3
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• pp.25-53
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• 2013

This study analyzes the effect of Computer Aided Innovation (CAI) to improve R&D Capabilities empirically. Survey was distributed by e-mail and Google Docs, targeting CTO of 235 SMEs. 142 surveys were returned back (rate of return 60.4%) from companies. Survey results from 119 companies (83.8%) which are effective samples except no-response, insincere response, estimated value, etc. were used for statistics analysis. Companies with less than 50billion KRW sales of entire researched companies occupy 76.5% in terms of sample traits. Companies with less than 300 employees occupy 83.2%. In terms of the type of company business Partners (called 'partners with big companies' hereunder) who work with big companies for business occupy 68.1%. SMEs based on their own business (called 'independent small companies') appear to occupy 31.9%. The present status of holding IT system according to traits of company business was classified into partners with big companies versus independent SMEs. The present status of ERP is 18.5% to 34.5%. QMS is 11.8% to 9.2%. And PLM (Product Life-cycle Management) is 6.7% to 2.5%. The holding of 3D CAD is 47.1% to 21%. IT system-holding and its application of independent SMEs seemed very vulnerable, compared with partner companies of big companies. This study is comprised of IT infra and IT Utilization as CAI capacity factors which are independent variables. factors of R&D capabilities which are independent variables are organization capability, process capability, HR capability, technology-accumulating capability, and internal/external collaboration capability. The highest average value of variables was 4.24 in organization capability 2. The lowest average value was 3.01 in IT infra which makes users access to data and information in other areas and use them with ease when required during new product development. It seems that the inferior environment of IT infra of general SMEs is reflected in CAI itself. In order to review the validity used to measure variables, Factors have been analyzed. 7 factors which have over 1.0 pure value of their dependent and independent variables were extracted. These factors appear to explain 71.167% in total of total variances. From the result of factor analysis about measurable variables in this study, reliability of each item was checked by Cronbach's Alpha coefficient. All measurable factors at least over 0.611 seemed to acquire reliability. Next, correlation has been done to explain certain phenomenon by correlation analysis between variables. As R&D capabilities factors which are arranged as dependent variables, organization capability, process capability, HR capability, technology-accumulating capability, and internal/external collaboration capability turned out that they acquire significant correlation at 99% reliability level in all variables of IT infra and IT Utilization which are independent variables. In addition, correlation coefficient between each factor is less than 0.8, which proves that the validity of this study judgement has been acquired. The pair with the highest coefficient had 0.628 for IT utilization and technology-accumulating capability. Regression model which can estimate independent variables was used in this study under the hypothesis that there is linear relation between independent variables and dependent variables so as to identify CAI capability's impact factors on R&D. The total explanations of IT infra among CAI capability for independent variables such as organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability are 10.3%, 7%, 11.9%, 30.9%, and 10.5% respectively. IT Utilization exposes comprehensively low explanatory capability with 12.4%, 5.9%, 11.1%, 38.9%, and 13.4% for organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability respectively. However, both factors of independent variables expose very high explanatory capability relatively for technology-accumulating capability among independent variable. Regression formula which is comprised of independent variables and dependent variables are all significant (P<0.005). The suitability of regression model seems high. When the results of test for dependent variables and independent variables are estimated, the hypothesis of 10 different factors appeared all significant in regression analysis model coefficient (P<0.01) which is estimated to affect in the hypothesis. As a result of liner regression analysis between two independent variables drawn by influence factor analysis for R&D capability and R&D capability. IT infra and IT Utilization which are CAI capability factors has positive correlation to organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability with inside and outside which are dependent variables, R&D capability factors. It was identified as a significant factor which affects R&D capability. However, considering adjustable variables, a big gap is found, compared to entire company. First of all, in case of partner companies with big companies, in IT infra as CAI capability, organization capability, process capability, human resources capability, and technology capability out of R&D capacities seems to have positive correlation. However, collaboration capability appeared insignificance. IT utilization which is a CAI capability factor seemed to have positive relation to organization capability, process capability, human resources capability, and internal/external collaboration capability just as those of entire companies. Next, by analyzing independent types of SMEs as an adjustable variable, very different results were found from those of entire companies or partner companies with big companies. First of all, all factors in IT infra except technology-accumulating capability were rejected. IT utilization was rejected except technology-accumulating capability and collaboration capability. Comprehending the above adjustable variables, the following results were drawn in this study. First, in case of big companies or partner companies with big companies, IT infra and IT utilization affect improving R&D Capabilities positively. It was because most of big companies encourage innovation by using IT utilization and IT infra building over certain level to their partner companies. Second, in all companies, IT infra and IT utilization as CAI capability affect improving technology-accumulating capability positively at least as R&D capability factor. The most of factor explanation is low at around 10%. However, technology-accumulating capability is rather high around 25.6% to 38.4%. It was found that CAI capability contributes to technology-accumulating capability highly. Companies shouldn't consider IT infra and IT utilization as a simple product developing tool in R&D section. However, they have to consider to use them as a management innovating strategy tool which proceeds entire-company management innovation centered in new product development. Not only the improvement of technology-accumulating capability in department of R&D. Centered in new product development, it has to be used as original management innovative strategy which proceeds entire company management innovation. It suggests that it can be a method to improve technology-accumulating capability in R&D section and Dynamic capability to acquire sustainable competitive advantage.

• Journal of Internet Computing and Services
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• v.18 no.4
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• pp.121-131
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• 2017

With emergence of Internet, social media, and mobile service, the consumers have actively presented their opinions and sentiment, and then it is spreading out real time as well. The user-generated text data on the Internet and social media is not only the communication text among the users but also the valuable resource to be analyzed for knowing the users' intent and sentiment. In special, economic participants have strongly asked that the social big data and its' analytics supports to recognize and forecast the economic trend in future. In this regard, the governments and the businesses are trying to apply the social big data into making the social and economic solutions. Therefore, this study aims to reveal the capability of social big data analysis for the economic use. The research proposed a social big data analysis model and an online consumer sentiment index. To test the model and index, the researchers developed an economic survey ontology, defined a sentiment dictionary for sentiment analysis, conducted classification and sentiment analysis, and calculated the online consumer sentiment index. In addition, the online consumer sentiment index was compared and validated with the composite consumer survey index of the Bank of Korea.

• 주택과사람들
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• s.198
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• pp.86-87
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• 2006

판교 등 주변 택지지구 개발로 인해 연일 집값이 들썩이고 있다. 게다가 용인 지역의 신봉·동천·성복·흥덕 등 4개 택지지구 분양이 올 연말과 내년 초로 대거 연기되면서, 수요자들의 관심이 더욱 뜨거워지고 있다. 이들 4개 택지지구의 입지 여건과 분양 계획에 대해 알아본다.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.6
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• pp.185-194
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• 2016

Recently, as the popularization of the mobile and the internet, the need of big data technology using a vast amount of data which contains the information has emerged. Big data technology has been used in various fields but use of the public sector is still insufficient. So, this study applies them. This study explores factors influencing silver industry as keywords, graving has effect on the present as well as future society. Results, five variables are 'silver Industry', 'senior citizen who lives alone', 'aging', 'birth' and 'retirement' were searched, and it was confirmed that they are correlated with one another. Results of analyzing the influence of the other four parameters on "Silver Industry", they have an effect significantly. In addition, it proposed the need of the 'providing living space of senior citizen who lives alone', 'childbirth support policy', 'support to vitalize silver startup senior manpower of technology' as an alternative to develop the silver industry. This study provided the theoretical implications that is exploring factors through a quantitative approach using big data and the practical implication is to suggest an alternative.

• Informatization Policy
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• v.19 no.4
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• pp.63-82
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• 2012

The world's best level of ICT(Information, Communication and Technology) infrastructure has experienced the world's worst level of ICT accident in Korea. The number of major accidents of privacy invasion has been three times larger than the total number of Internet user of Korea. The cause of the severe accident was due to big data environment. As a result, big data environment has become an important policy agenda. This paper has conducted analyzing the accident case of data spill to study policy issues for ICT security from a social science perspective focusing on risk. The results from case analysis are as follows. First, ICT risk can be categorized 'severe, strong, intensive and individual'from the level of both probability and impact. Second, strategy of risk management can be designated 'avoid, transfer, mitigate, accept' by understanding their own culture type of relative group such as 'hierarchy, egalitarianism, fatalism and individualism'. Third, personal data has contained characteristics of big data such like 'volume, velocity, variety' for each risk situation. Therefore, government needs to establish a standing organization responsible for ICT risk policy and management in a new big data era. And the policy for ICT risk management needs to balance in considering 'technology, norms, laws, and market'in big data era.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.268-270
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• 2019

In this paper, we analyzed the papers related to the "4th Industry". In order to analyze the papers, total of 685 papers were collected by searching with the keyword "4th industry" in Korea Journal Index(KCI) from 2016 to 2019. We used Python-based web scraping program to collect papers. As a result of analysis, it was confirmed that artificial intelligence, big data, Internet of things(IoT), digital, network and so on have emerged as the major technologies, and it was confirmed that research has been utilizing the major technologies in various fields related to the 4th industry such as industry, government, education field, and job.

• Korean small business review
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• v.42 no.4
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• pp.123-146
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• 2020

This study, targeting KOSDAQ-listed companies, examined the relationship between variability of accruals and corporate characteristics. First, the analysis results show that executives of companies with high debt ratios are more likely to violate debt contracts, so there is a strong temptation to use discretionary accrual items. Second, for companies with large volatility in operating cash flows, Executives of these companies are strongly inclined to utilize accruals for the purpose of abuse of discretion. Third, the larger the company, the more sensitive it is to political costs, so it is less tempted to use the accruals item than a smaller company. Fourth, the corporate age is thought to be the maturity of the company, Executives of such companies have little room to use accruals to abuse their discretion. Fifth, in the case of profit dummy variables, the companies reporting losses have more temporary accrual items than those reporting profits, so this increases the uncertainty in their accounting information than the latter. Sixth, for those companies that are indicated as inappropriate as a result of audit, the more likely their executives are to use the accrual items, and the lower the quality of their accounting profits is. Lastly, Companies audited by 4 Big domestic accounting firms have less discretionary accrual fluctuations than companies audited by non-big 4 accounting firms. Thus, it was found that the accrual amount allows the discretion of corporate executives differently according to the characteristics of the company.