• Journal of Digital Contents Society
• /
• v.19 no.3
• /
• pp.529-535
• /
• 2018

Data is explosively growing, but many companies are still using data analysis only for descriptive analysis or diagnostic analysis, and not appropriately for predictive analysis or enterprise technology strategy analysis. In this study, we analyze the structured & unstructured patent data such as IPC code, inventor, filing date and so on by using big data analysis techniques such as network analysis and TF-IDF. Through this analysis, we propose analysis process to understand the core technology and technology distribution of competitors and prove it through data analysis.

• The Journal of Asian Finance, Economics and Business
• /
• v.6 no.2
• /
• pp.45-54
• /
• 2019

This study examines whether auditors restrain the analysts' opportunistic behavior as reviewing the companies' interim reports. Analysts' forecasts show a walkdown pattern in which their optimism has decreased as the earnings announcement date has approached. At the beginning of the year, there is a lack of high-quality benchmark information that enables information users to judge the accuracy of analyst's earnings forecasts. Thus, early in the year, analysts are highly inspired to disseminate optimistic forecasts in order to gain manager's favor. In this study, we examine adequate benchmarks prevent analysts from disclosing optimistically biased forecasts. We conjecture that auditors' efforts might mitigate analysts' walkdown pattern. To test this hypothesis, we use data from Korea, where it is mandatory to disclose auditor's review hours. We find that the analyst forecast's walkdown decreases with the ratio as well as the number of audit hours. It implies that an auditor's effort in reviewing interim financial information has a monitoring function that reduces analysts' opportunistic optimism at the beginning of the year. We conjecture that the tendency will be more pronounced when BIG4 auditors review the interim reports. Consistent with the prediction, BIG4 auditors' interim review effort is more effective in suppressing the analysts' walkdown.

• Journal of Korean Medicine Rehabilitation
• /
• v.29 no.4
• /
• pp.135-142
• /
• 2019

The objective of this study is to propose Korean Medicine treatment for recurrent limb weakness after Guillain-Barre syndrome (GBS) improvement by intraveinous immunoglobulin, and to report its effectiveness. Manual muscle test (MMT), Korean modified Bathel index (K-MBI), and tendon reflex were used to evaluate the patient. The patient was improved hip joint, knee joint, ankle joint MMT from grade 3-/3- to grade 5/5 and in the upper limb the patient can do big joint exercise but cannot do micromovement like writing or using cell phone. When discharge date the patient's wrist joint MMT grade is improved grade 5-/5- to grade 5/5. The K-MBI score is improved from 71 to 86 and there was a big change in walking and chair/bed transfer, there was no change in tendon reflex. This study suggests that Korean Medicine can be effective for patients who have recurrent limb weakness after GBS improvement.

• Communications for Statistical Applications and Methods
• /
• v.30 no.5
• /
• pp.485-499
• /
• 2023

This study aimed to predict the number of meals served in a group cafeteria using machine learning methodology. Features of the menu were created through the Word2Vec methodology and clustering, and a stacking ensemble model was constructed using Random Forest, Gradient Boosting, and CatBoost as sub-models. Results showed that CatBoost had the best performance with the ensemble model showing an 8% improvement in performance. The study also found that the date variable had the greatest influence on the number of diners in a cafeteria, followed by menu characteristics and other variables. The implications of the study include the potential for machine learning methodology to improve predictive performance and reduce food waste, as well as the removal of subjective elements in menu classification. Limitations of the research include limited data cases and a weak model structure when new menus or foreign words are not included in the learning data. Future studies should aim to address these limitations.

• Convergence Security Journal
• /
• v.22 no.2
• /
• pp.115-120
• /
• 2022

This study analyzed the recent trends in the sports environment to which big data and AI technologies, which are representative technologies of the 4th Industrial Revolution, and approached them from the perspective of convergence of big data and AI technologies in the sports field. And the results are as follows. First, it is being used for player and game data analysis and team strategy establishment and operation. Second, by combining big data collected using GPS, wearable equipment, and IoT with artificial intelligence technology, scientific physical training for each player is possible through user individual motion analysis, which helps to improve performance and efficiently manage injuries. Third, with the introduction of an AI-based judgment system, it is being used for judge judgment. Fourth, it is leading the change in marketing and game broadcasting services. The technology of the 4th Industrial Revolution is bringing innovative changes to all industries, and the sports field is also in the process. The combination of big data and AI is expected to play an important role as a key technology in the rapidly changing future in a sports environment where scientific analysis and training determine victory or defeat.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.11
• /
• pp.2067-2075
• /
• 2017

Lately big data processing is considered as an emerging issue. As a huge amount of data is generated, data processing capability is getting important. In processing big data, both Hadoop distributed file system and unstructured date processing-based NoSQL data store are getting a lot of attention. However, there still exists problems and inconvenience to use NoSQL. In case of low volume data, MapReduce of NoSQL normally consumes unnecessary processing time and requires relatively much more data retrieval time than RDBMS. In order to address the NoSQL problem, in this paper, an interworking scheme between NoSQL and the conventional RDBMS is proposed. The developed auto-mapping scheme enables to choose an appropriate database (NoSQL or RDBMS) depending on the amount of data, which results in fast search time. The experimental results for a specific data set shows that the database interworking scheme reduces data searching time by 35% at the maximum.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.23 no.1
• /
• pp.159-169
• /
• 1999

The purpose of this study was to study real body size and ideal body shape by considering the relations of between the degree of satisfaction and consciousness of body cathexis and real body size and to understand change of the degree of satisfaction at body part and ideal body shape with the changes of the times by comparing the date taken at 1997 to that taken at 1992. The subjects in this study were 542 and 201 female college students aged from 18 to 25 Body. measurements and survery were taken from April to June 1997, and May to June 1992 Data were analyzed by correlation analysis ANOVA duncan test and crosstabulation analysis The results were as follows : (1) The subjects tended to be satisfied with long and slim limbs silm trunk and preferred to be tall in height and light in weight. But exceptionally they tended to be unstatisfied with small bust as well as big one. (2) The subjects want to be slimmer lower body than upper body. (3) The subjects of 1997 was more statisfied with tall height light weight slim limbs and narrow shoulder than those of 1992. (4) The ideal body shape of the subject of 1997 was slim body while that of the subject of 1992 was big bust and wasp waist. (5) The subject of 1997 was more satisfied with leanner body than that of 1992.

• Korean Journal of Construction Engineering and Management
• /
• v.18 no.5
• /
• pp.20-31
• /
• 2017

As global interests in the 4th Industrial Revolution have recently increased, it becomes critical for the construction industry to pro-actively cope with it. For effective actions, the construction industry needs to make active use of 4th Industrial Revolution technologies based on the up-to-date understanding of the trends of construction technology development employing the 4th Industrial Revolution technologies. The objective of the study is to investigate and identify key trends of ICT construction technology development over the last ten years based on Big Data Analytics. The study identifies eleven key trends and discusses that ICT construction technology development has not been as active as expected and software technologies have been less developed compared to hardware technologies.

• Journal of the Korea Convergence Society
• /
• v.13 no.2
• /
• pp.1-12
• /
• 2022

The purpose of this study is to identify the social perception characteristics of young children with disabilities over the past decade. For this purpose, Textom, an Internet-based big data analysis system was used to collect data related to young children with disabilities posted on social media. 50 keywords were selected in the order of high frequency through the data cleaning process. For semantic network analysis, centrality analysis and CONCOR analysis were performed with UCINET6, and the analyzed data were visualized using NetDraw. As a result, the keywords such as 'education, needs, parents, and inclusion' ranked high in frequency, degree, and eigenvector centrality. In addition, the keywords of 'parent, teacher, problem, program, and counseling' ranked high in betweenness centrality. In CONCOR analysis, four clusters were formed centered on the keywords of 'disabilities, young child, diagnosis, and programs'. Based on these research results, the topics on social perception of young children with disabilities were investigated, and implications for each topic were discussed.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.10
• /
• pp.137-146
• /
• 2022

In relation to the travel itinerary recommendation service, which has recently become in high demand, our previous work introduces a method to quantify the popularity of places including tour spots, restaurants, and accommodations through social big data analysis, and to create a travel schedule based on the analysis results. On the other hand, the generated schedule was mainly composed of travel routes that connected tour spots with the shorted distance, and detailed schedule information including restaurants and accommodation information for each travel date was not provided. This paper presents an algorithm for constructing a detailed travel route using a scenario template in a travel schedule created based on social big data, and introduces a prototype system that implements it. The proposed system consists of modules such as place information collection, place-specific popularity score estimation, shortest travel rout generation, daily schedule organization, and UI visualization. Experiments conducted based on social reviews collected from 63,000 places in the Gyeongnam province proved effectiveness of the proposed system.