• Journal of Information Technology Applications and Management
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• v.26 no.5
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• pp.57-65
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• 2019

Recommendation Systems are information technologies that E-commerce merchants have adopted so that online shoppers can receive suggestions on items that might be interesting or complementing to their purchased items. These systems stipulate valuable assistance to the user's purchasing decisions, and provide quality of push service. Traditionally, Recommendation Systems have been designed using a centralized system, but information service is growing vast with a rapid and strong scalability. The next generation of information technology such as Cloud Computing and Big Data Environment has handled massive data and is able to support enormous processing power. Nevertheless, analytic technologies are lacking the different capabilities when processing big data. Accordingly, we are trying to design a conceptual service model with a proposed new algorithm and user adaptation on dynamic recommendation service for big data environment.

• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.470-476
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• 2015

Big data has been used in diverse areas. For example, in computer science and sociology, there is a difference in their issues to approach big data, but they have same usage to analyze big data and imply the analysis result. So the meaningful analysis and implication of big data are needed in most areas. Statistics and machine learning provide various methods for big data analysis. In this paper, we study a process for big data analysis, and propose an efficient methodology of entire process from collecting big data to implying the result of big data analysis. In addition, patent documents have the characteristics of big data, we propose an approach to apply big data analysis to patent data, and imply the result of patent big data to build R&D strategy. To illustrate how to use our proposed methodology for real problem, we perform a case study using applied and registered patent documents retrieved from the patent databases in the world.

• The Korean Journal of Elementary Counseling
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• v.11 no.3
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• pp.371-390
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• 2012

This study examined the reliability and validity of the Korean short version of the Big-Five Questionnaire for children, a instrument designed to measure Big-Five personality domains of elementary school students. The short Big-Five Questionnaire for children was composed of 15 items based on exploratory factor analyses on th data from 5th and 6th grade elementary school students(N=278). Confirmatory factor analyses revealed evidence of structural validity of the Korean short version BFQ-C. The correlations of K-BFQC-SF with the criteria of depression, academic achievement, career maturity were assessed to verify criterion-related validity. The correlation coefficients were correspondent to the results of previous studies. This study is meaningful in that it is sufficient to assess five factor personality domains in school settings.

• ETRI Journal
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• v.34 no.5
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• pp.783-786
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• 2012

This letter proposes a more advanced joint maximum a posteriori (MAP) adaptation using a prior model based on a probabilistic scheme utilizing the bilinear transformation (BIT) concept. The proposed method not only has scalable parameters but is also based on a single prior distribution without the heuristic parameters of the previous joint BIT-MAP method. Experiment results, irrespective of the amount of adaptation data, show that the proposed method leads to a consistent improvement over the previous method.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.1
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• pp.213-231
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• 2023

The purpose of this study is to develop and present a model that can evaluate the data maturity of library. To achieve this goal, library data maturity model can be applied to library was designed by analyzing previous studies related to data maturity. As a result of this study, proposed data maturity model consisting of 19 evaluation factors in 5 areas was designed, and the maturity level was set to 5 levels. In the future, it will be possible to measure the data maturity of libraries participating in the library big data project using the data maturity evaluation model, and it can be expected that in the long term, it will be possible to present a direction for data-based library operation and data utilization development.

• Journal of Convergence Society for SMB
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• v.5 no.4
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• pp.11-16
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• 2015

In recent years, small businesses are increasing attempt to create better value through a combination of benefits with small and flexible organization of big data. However, until now small businesses are lacking to secure sustainable competitiveness to match the ICT paradigm alteration to focus on improving productivity. This paper propose an efficient small businesses process model which can effectively take advantage of a low cost, identify customer needs, taget marketing, customer management for new product. Proposed model can retain the necessary competitiveness in generating new business for collaboration between companies inside and companies using a massive big data. Also, proposed model can be utilized the overall business activities such as the target customer selection, pricing strategies, public relations and promotional activities and enhanced new product development capabilities using big data.

• Journal of Digital Convergence
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• v.11 no.8
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• pp.31-39
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• 2013

Recently, integration between social media and the industry has been expended, and as the usage of Internet through various smart devices of not only the existing PC but also smart phone, tablet PC and so on, a lot of unstructured data has occurred, leading to increased interest on big data system. According to the institutes which specialize in market research, the data amount is predicted to increase by 9 folds in the next 5 years when compared to the present, and the big data market is also expected to grow bigger. This dissertation evaluates the efficiency test of big data through analysis on the requirements by identifying and fragmenting the items of efficiency quality evaluation that big data should be equipped with.

• Journal of the Korean Society for Heat Treatment
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• v.35 no.6
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• pp.327-336
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• 2022

In this study, a model for automatically recognizing several steel parts through a camera before charging materials was developed under the assumption that the temperature distribution in the pre-air atmosphere was known. For model development, datasets were collected in random environments and factories. In this study, the YOLO-v5 model, which is a YOLO model with strengths in real-time detection in the field of object detection, was used, and the disadvantages of taking a lot of time to collect images and learning models was solved through the transfer learning methods. The performance evaluation results of the derived model showed excellent performance of 0.927 based on mAP 0.5. The derived model will be applied to the model development study, which uses the model to accurately recognize the material and then match it with the temperature distribution in the atmosphere to determine whether the material layout is suitable before charging materials.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.9-16
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• 2023

In the IoT environment, various objects mutually interactive, and various services can be composed based on this environment. In the previous study, we have developed a resource collaboration system to provide services by substituting limited resources in the user's personal device using resource collaboration. However, in the preceding system, when the number of resources and situations increases, the inference time increases exponentially. To solve this problem, this study proposes a method of classifying users and resources by applying the BIG5 user type classification model. In this paper, we propose a method to reduce the inference time by filtering the user's preferred resources through BIG5 type-based preprocessing and using the filtered resources as an input to the recommendation system. We implement the proposed method as a prototype system and show the validation of our approach through performance and user satisfaction evaluation.