• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.97-99
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• 2021

본 논문에서는 극장 비수기 기간 효율적인 상영을 위한 재개봉 영화 도출과 영화관 입장권 통합 전산망을 기반으로 극장 산업과 OTT 산업에서 제공하는 시청각 콘텐츠의 소비자 선호도를 분석한다. 기존 재개봉 영화는 연휴와 같은 성수기 바로 전 비수기 기간에 집중적으로 상영되고 있다. 즉 재개봉 영화 상영은 대형 영화 개봉 전 공백을 메우기 위해 상영되고 있음을 의미한다. 재개봉 영화는 대부분 예술 영화를 상영하고 연도마다 일정한 수요를 보이고 있다. 이러한 기조는 코로나 19 전까지 변함없이 이어졌으나, 코로나 19 이후 재개봉 영화에 대한 수요가 다른 년도 같은 월에 비해 급증하였다. 영화 산업의 전반적인 침체와 달리 재개봉 영화에 대한 수요는 늘어난 것이다. 코로나 19가 장기화되는 만큼 본 논문에서는 영화관 입장권 통합 전산망 데이터를 중심으로 영화 산업과 OTT 산업 이용자들의 선호 콘텐츠를 분석하고 기존 재개봉 영화와 대조하여 지속적이고 효율적 상영을 위한 재개봉 영화를 제안한다.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.75-87
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• 2023

We designed to employ an Artificial Intelligence learning model to predict real estate prices and determine the reasons behind their changes, with the goal of using the results as a guide for policy. Numerous studies have already been conducted in an effort to develop a real estate price prediction model. The price prediction power of conventional time series analysis techniques (such as the widely-used ARIMA and VAR models for univariate time series analysis) and the more recently-discussed LSTM techniques is compared and analyzed in this study in order to forecast real estate prices. There is currently a period of rising volatility in the real estate market as a result of both internal and external factors. Predicting the movement of real estate values during times of heightened volatility is more challenging than it is during times of persistent general trends. According to the real estate market cycle, this study focuses on the three times of extreme volatility. It was established that the LSTM, VAR, and ARIMA models have strong predictive capacity by successfully forecasting the trading price index during a period of unusually high volatility. We explores potential synergies between the hybrid artificial intelligence learning model and the conventional statistical prediction model.

• Journal of Industrial Convergence
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• v.21 no.10
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• pp.21-28
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• 2023

As the need for the use of big data increases, various analysis activities using big data, including SNS data, are being carried out in individuals, companies, and countries. However, existing research on predicting technology market trends has been mainly conducted using expert-dependent or patent or literature research-based data, and objective technology prediction using big data is needed. Therefore, this study aims to present a model for predicting future technologies through decision tree analysis, visualization analysis, and percentage analysis with data from social network services (SNS). As a result of the study, percentage analysis was better able to predict positive techniques compared to other analysis results, and visualization analysis was better able to predict negative techniques compared to other analysis results. The decision tree analysis was also able to make meaningful predictions.

• Journal of Digital Convergence
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• v.17 no.7
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• pp.35-41
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• 2019

This study aims to analyze the cosmedical cosmetics market and the nature of customer through the social big data analysis. More than 80,000 posts were analyzed using R program. After data cleansing, keyword frequency analysis and association analysis were performed to understand customer needs and competitor positioning, formulated several implications for marketing strategy sophistication and implementation. Analysis results show that "prevention" is a new and essential attribute for appealing target customers. The expansion of the product line for the gift market is also suggested. It has been shown that there is a high correlation with products that can be complementary to each other. In addition to the traditional marketing technique, the social big data analysis based on evidence was useful in deriving the characteristics of the customers and the market that had not been identified before. Word2vec algorithm will be beneficial to find additional.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.10
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• pp.1071-1078
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• 2014

Recently, information providing service using Big Data is being expanded. Big Data processing technology is actively being academic research to an important issue in the IT industry. In this paper, we analyze a skilled pattern of welder through Big Data analysis or extraction of welding based on R programming. We are going to reduce cost on welding work including weld quality, weld operation time by providing analyzed results non-skilled welder. Welding has a problem that should be invested long time to be a skilled welder. For solving these issues, we apply connection rules algorithms and regression method to much pattern variable for welding pattern analysis of skilled welder. We analyze a pattern of skilled welder according to variable of analyzed rules by analyzing top N rules. In this paper, we confirmed the pattern structure of power consumption rate and wire consumption length through experimental results of analyzed welding pattern analysis.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.28 no.3
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• pp.67-93
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• 2017

This study aims to investigate research trends on disaster safety Information based on the language network analysis methods. To accomplish it, we collected 312 Korean thesis and scholarly articles on disaster safety information published between 2008 and 2017 from RISS (Research Information Sharing Service) site. With the collected data, this study performed the statistical analysis based on bibliographic data. Also, this study performed the analysis of frequency and language network on keyword extracted from titles on the collected scholarly articles and thesis. This study found out that researches recently on Bigdata related to disaster safety information have been rapidly increased. Also, the needs of sharing and utilizing disaster safety information have increased. Also the various types of disaster safety information such as spatial data, real-time information, geographic information has been used for the disaster response.

• The Journal of Industrial Distribution & Business
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• v.11 no.5
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• pp.45-52
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• 2020

Purpose: In the 4th industrial revolution, services that incorporate various smart technologies in the tourism sector have begun to gain popularity. Accordingly, academic discussions on smart tourism have also started to become active in various fields. Despite recent research, the definition of smart tourism is still ambiguous, and it is not easy to differentiate its scope or characteristics from traditional tourism concepts. Thus, this study aims to analyze the perception of smart tourism exposed online to identify the current point of smart tourism in Korea and present the research direction for conceptualizing smart tourism suitable for the domestic situation. Research design, data, and methodology: This study analyzes the perception of smart tourism exposed online based on 20,198 news data from portal sites over the past six years. Data on words used with smart tourism were collected from the leading portal sites Naver, Daum, and Google. Text mining techniques were applied to identify the social awareness status of smart tourism. Network analysis was used to visualize the results between words related to smart tourism, and CONCOR analysis was conducted to derive clusters formed by words having similarity. Results: As a result of keyword analysis, the frequency of words related to the development and construction of smart tourism areas was high. The analysis of the centrality of the connection between words showed that the frequency of keywords was similar, and that the words "smartphones" and "China" had relatively high connection centrality. The results of network analysis and CONCOR indicated that words were formed into eight groups including related technologies, promotion, globalization, service introduction, innovation, regional society, activation, and utilization guide. The overall results of data analysis showed that the development of smart tourism cities was a noticeable issue. Conclusions: This study is meaningful in that it clearly reflects the differences in the perception of smart tourism between online and research trends despite various efforts to develop smart tourism in Korea. In addition, this study highlights the need to understand smart tourism concepts and enhance academic discussions. It is expected that such academic discussions will contribute to improving the competitiveness of smart tourism research in Korea.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.219-227
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• 2020

Smart Factory is one of the fastest developing and changing fourth industrial revolution fields. In particular, the degree of introduction and maturity level in the smart factory is an important part. In this paper, a cluster analysis of companies introduced smart factory was performed based on a new maturity assessment model. The 68% of 193 companies surveyed were at the basic level, with only 21% being the middle one. Most SMEs cited lack of funds as the main reason for not entering the middle one. As a result of the cluster analysis, it was found that all clusters had similar patterns but grouped into one of three levels of high, middle, and low depending on maturity level of smart factory operation, and process domain had the highest maturity and data domain was lowest among the 6 domains. Through this, analysis of more specific and quantified maturity levels can be performed using 6-domain smart factory maturity evaluation model.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.935-940
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• 2023

Most text data collected through web scraping for artificial intelligence and big data analysis is generally large and unstructured, so a purification process is required for big data analysis. The process becomes structured data that can be analyzed through a heuristic pre-processing refining step and a post-processing machine refining step. Therefore, in this study, in the post-processing machine refining process, the Korean dictionary and the stopword dictionary are used to extract vocabularies for frequency analysis for word cloud analysis. In this process, "user-defined stopwords" are used to efficiently remove stopwords that were not removed. We propose a methodology for applying the "thesaurus" and examine the pros and cons of the proposed refining method through a case analysis using the "user-defined stop word thesaurus" technique proposed to complement the problems of the existing "stop word dictionary" method with R's word cloud technique. We present comparative verification and suggest the effectiveness of practical application of the proposed methodology.

• Journal of Digital Convergence
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• v.15 no.5
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• pp.61-69
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• 2017

This study aims to investigate the public big data standard analysis model developed by Ministry of the Interior and examine its accuracy and reliability of prediction. To do this, big data standard analysis index were calculated to apply them to the real world case of CCTV monitoring system prior installation in K city. The result of this case study revealed that the areas to be installed CCTV consisted with the area where residences requested and complained to install CCTV monitoring systems, which indicated that the result of big data standard analysis model provided accurate and reliable outcomes. The result of this study suggested implications on effective exploitation of big data analysis.