• Journal of Korea Technology Innovation Society
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• v.22 no.2
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• pp.265-281
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• 2019

With the advent of the Internet-based economy, the dramatic changes in consumption patterns have been witnessed during the last decades. The seminal change has led by Data Technology, the integrated platform of mobile, online, offline and artificial intelligence, which remained unchallenged. In this paper, I use data analysis tool (TexTom) in order to articulate the definitfite notion of data technology from Internet sources. The data source is collected for last three years (November 2015 ~ November 2018) from Google and Naver. And I have derived several key keywords related to 'Data Technology'. As a result, it was found that the key keyword technologies of Big Data, O2O (Offline-to-Online), AI, IoT (Internet of things), and cloud computing are related to Data Technology. The results of this study can be used as useful information that can be referred to when the Data Technology age comes.

• Journal of Information Technology Services
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• v.21 no.6
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• pp.39-51
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• 2022

As the 4th Industrial Revolution emerged as a key to improving national competitiveness, OCR technology, one of the major technologies in the 4th industry is in the spotlight. Since characters in various images contain a lot of information, OCR technology for recognizing these characters has evolved into technology used in many industries. In this paper, trends in OCR technology were identified and predicted using thesis data published in 'RISS' and patent data by International patent classification (IPC) under the theme of Optical character recognition (OCR). For patent data 20,000 patents related to OCR technology from 2002 to 2020 were used as data, and 432 papers from 2012 to 2022 were used as data. Through time-series analysis, each patent data and thesis data were investigated since when OCR technology has developed, and various keyword analysis predicted which technology will be used in the future. Finally, the direction of future OCR technology development was presented through network association analysis with patent data and thesis data.

• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.113-120
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• 2015

Technology analysis is to analyze technological data such as patent and paper for a given technology field. From the results of technology analysis, we can get novel knowledge for R&D planing and management. For the technology analysis, we can use diverse methods of statistics. Time series analysis is one of efficient approaches for technology analysis, because most technologies have researched and developed depended on time. So many technological data are time series. Time series data are occurred through time. In this paper, we propose a methodology of technology forecasting using the dynamic time warping (DTW) of time series analysis. To illustrate how to apply our methodology to real problem, we perform a case study of patent documents in target technology field. This research will contribute to R&D planning and technology management.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.237-245
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• 2017

Our research is aimed at predicting recent trend and leading technology for the future and providing optimal Nano technology trend information by analyzing Nano technology trend. Under recent global market situation, Users' needs and the technology to meet these needs are changing in real time. At this point, Nano technology also needs measures to reduce cost and enhance efficiency in order not to fall behind the times. Therefore, research like trend analysis which uses search data to satisfy both aspects is required. This research consists of four steps. We collect data and select keywords in step 1, detect trends based on frequency and create visualization in step 2, and perform analysis using data mining in step 3. This research can be used to look for changes of trend from three perspectives. This research conducted analysis on changes of trend in terms of major classification, Nano technology of 30's, and key words which consist of relevant Nano technology. Second, it is possible to provide real-time information. Trend analysis using search data can provide information depending on the continuously changing market situation due to the real-time information which search data includes. Third, through comparative analysis it is possible to establish a useful corporate policy and strategy by apprehending the trend of the United States which has relatively advanced Nano technology. Therefore, trend analysis using search data like this research can suggest proper direction of policy which respond to market change in a real time, can be used as reference material, and can help reduce cost.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.112-122
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• 2022

Food security and its sovereignty have become among the most important key issues due to changes in the international situation. Regarding these issues, many countries now give attention to smart agriculture, which would increase production efficiency through a data-based system. The Korean government also has attempted to promote smart agriculture by 1) implementing the agri-food ICT (information and communications technology) policy, and 2) increasing the R&D budget by more than double in recent years. However, its endeavors only centered on large-scale farms which a number of domestic farmers rarely utilized in their farming. To promote smart agriculture more effectively, we diagnosed the government R&D trends of smart agriculture based on NTIS (National Science and Technology Information Service) data. We identified the research trends for each R&D period by analyzing three pieces of information: the regional information, research actor, and topic. Based on these findings, we could suggest systematic R&D directions and implications.

• The Journal of Information Systems
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• v.27 no.1
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• pp.171-191
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• 2018

Purpose Corporate technology leakage is not only monetary loss, but also has a negative impact on the corporate image and further deteriorates sustainable growth. In particular, since SMEs are highly dependent on core technologies compared to large corporations, loss of technology leakage threatens corporate survival. Therefore, it is important for SMEs to "prevent and protect technology leakage". With the recent development of data analysis technology and the opening of public data, it has become possible to discover and proactively detect companies with a high probability of technology leakage based on actual company data. In this study, we try to construct profiles of enterprises with and without technology leakage experience through profiling analysis using data mining techniques. Furthermore, based on this, we propose a classification model that distinguishes companies that are likely to leak technology. Design/methodology/approach This study tries to develop the empirical model for prevention and protection of technology leakage through profiling method which analyzes each SME from the viewpoint of individual. Based on the previous research, we tried to classify many characteristics of SMEs into six categories and to identify the factors influencing the technology leakage of SMEs from the enterprise point of view. Specifically, we divided the 29 SME characteristics into the following six categories: 'firm characteristics', 'organizational characteristics', 'technical characteristics', 'relational characteristics', 'financial characteristics', and 'enterprise core competencies'. Each characteristic was extracted from the questionnaire data of 'Survey of Small and Medium Enterprises Technology' carried out annually by the Government of the Republic of Korea. Since the number of SMEs with experience of technology leakage in questionnaire data was significantly smaller than the other, we made a 1: 1 correspondence with each sample through mixed sampling. We conducted profiling of companies with and without technology leakage experience using decision-tree technique for research data, and derived meaningful variables that can distinguish the two. Then, empirical model for prevention and protection of technology leakage was developed through discriminant analysis and logistic regression analysis. Findings Profiling analysis shows that technology novelty, enterprise technology group, number of intellectual property registrations, product life cycle, technology development infrastructure level(absence of dedicated organization), enterprise core competency(design) and enterprise core competency(process design) help us find SME's technology leakage. We developed the two empirical model for prevention and protection of technology leakage in SMEs using discriminant analysis and logistic regression analysis, and each hit ratio is 65%(discriminant analysis) and 67%(logistic regression analysis).

• Applied Microscopy
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• v.50
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• pp.2.1-2.7
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• 2020

Electron energy loss spectroscopy (EELS) is an analytical technique that can provide the structural, physical and chemical information of materials. The EELS spectra can be obtained by combining with TEM at sub-nanometer spatial resolution. However, EELS spectral information can't be obtained easily because in order to interpret EELS spectra, we need to refer to and/or compare many reference data with each other. And in addition to that, we should consider the different experimental variables used to produce each data. Therefore, reliable and easily interpretable EELS standard reference data are needed. Our Electron Energy Loss Data Center (EELDC) has been designated as National Standard Electron Energy Loss Data Center No. 34 to develop EELS standard reference (SR) data and to play a role in dissemination and diffusion of the SR data to users. EELDC has developed and collected EEL SR data for the materials required by major industries and has a total of 82 EEL SR data. Also, we have created an online platform that provides a one-stop-place to help users interpret quickly EELS spectra and get various spectral information. In this paper, we introduce EEL SR data, the homepage of EELDC and how to use them.

• Journal of the Korean Applied Science and Technology
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• v.37 no.1
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• pp.76-82
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• 2020

Big data analysis in healthcare research seems to be a necessary strategy for the convergence of sports science and technology in the era of the Fourth Industrial Revolution. The purpose of this study is to provide the basic review to secure the diversity of big data and healthcare convergence by discussing the concept, analysis method, and application examples of big data and by exploring the application. Text mining, data mining, opinion mining, process mining, cluster analysis, and social network analysis is currently used. Identifying high-risk factor for a certain condition, determining specific health determinants for diseases, monitoring bio signals, predicting diseases, providing training and treatments, and analyzing healthcare measurements would be possible via big data analysis. As a further work, the big data characteristics provide very appropriate basis to use promising software platforms for development of applications that can handle big data in healthcare and even more in sports science.

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

• Journal of the Korea Society of Computer and Information
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• v.21 no.8
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• pp.77-84
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• 2016

In general statistical analysis, we need to make a normal assumption. If this assumption is not satisfied, we cannot expect a good result of statistical data analysis. Most of statistical methods processing the outlier and noise also need to the assumption. But the assumption is not satisfied in big data because of its large volume and heterogeneity. So we propose a methodology based on box-plot and data smoothing for controling outlier and noise in big data analysis. The proposed methodology is not dependent upon the normal assumption. In addition, we select patent documents as target domain of big data because patent big data analysis is a important issue in management of technology. We analyze patent documents using big data learning methods for technology analysis. The collected patent data from patent databases on the world are preprocessed and analyzed by text mining and statistics. But the most researches about patent big data analysis did not consider the outlier and noise problem. This problem decreases the accuracy of prediction and increases the variance of parameter estimation. In this paper, we check the existence of the outlier and noise in patent big data. To know whether the outlier is or not in the patent big data, we use box-plot and smoothing visualization. We use the patent documents related to three dimensional printing technology to illustrate how the proposed methodology can be used for finding the existence of noise in the searched patent big data.