• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.87-99
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• 2013

Massive increases in data available to an organization are creating a new opportunity for competitive advantage. In this era of big data, developing analytics capabilities, therefore, becomes critical to take advantage of internal and external data and gain insights for data-driven decision making. However, the use of data in education is in its infancy, in comparison with business and government, and the potential for data analytics to impact education services is growing. In this paper, I survey how universities are currently using education data to improve students' performance and administrative efficiency, and propose new ways of extending the current use. In addition, with the so-called data scientist shortage, universities should be able to train professionals with data analytics skills. This paper discusses which skills are valuable to data scientists and introduces various training and certification programs offered by universities and industry. I finally conclude the paper by exploring new curriculums where students, by themselves, can learn how to find and use relevant data even in any courses.

• Journal of the Korea Convergence Society
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• v.8 no.4
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• pp.1-8
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• 2017

In the modern society, the size of the fashion market is continuously increasing both overseas and domestic. When purchasing a product through e-commerce, the evaluation data for the product created by other consumers has an effect on the consumer's decision to purchase the product. By analysing the consumer's evaluation data on the product the company can reflect consumer's opinion which can leads to positive affect of performance to company. In this paper, we propose a method to construct a model to analyze user's sentiment using word embedding space formed by learning review data of amazon fashion products. Experiments were conducted by learning three SVM classifiers according to the number of positive and negative review data using the formed word embedding space which is formed by learning 5.7 million Amazon review data.. Experimental results showed the highest accuracy of 88.0% when learning SVM classifier using 50,000 positive review data and 50,000 negative review data.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2008.06a
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• pp.75-81
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• 2008

오늘날 정부기관, 지자체 그리고 민간 기업 에서 3차원 지리정보데이터에 대한 필요성이 증가하고 있어 그에 대한 연구가 활발히 이루어지고 있다. 이러한 연구의 결과로 국내에서 3차원 지리정보 데이터 포맷인 3DF-GML(3DFeature-GML)이 개발되었다. 하지만 3DF-GML의 데이터는 기존의 Shape 파일만을 사용하여 구축되기 때문에 활용성에 제한을 받고 있다. 또한 제작 및 수정 시에는 제한된 애플리케이션 활용만이 허용된다. 이에 따라 3DF-GML의 활용성을 높이기 위해 국내의 데이터뿐 만이 아니라 국외에서 제작되어진 3차원 지리정보 데이터 포맷의 데이터를 활용할 수 있는 방안이 필요하다. 본 논문에서는 국내의 3차원 지리정보 포맷(3DF-GML)과 국외의 3차원 지리정보 포맷들간의 데이터 포맷을 비교 및 분석한다. 이러한 분석은 국외 3차원 지리정보 포맷의 데이터 활용 및 호환을 유도하는 것이 가능하다. 따라서 본 논문에서는 국내 3차원 지리 정보데이터(3DF-GML)와 국외 3차원 지리정보 데이터 간의 변환 규칙 및 방법을 제시한다.

• Journal of Internet Computing and Services
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• v.22 no.5
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• pp.79-86
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• 2021

As the usefulness of big data analysis has been drawing attention, many studies in the business research area begin to use big data to predict firm performance. Previous studies mainly rely on data outside of the firm through news articles and social media platforms. The voices within the firm in the form of employee satisfaction or evaluation of the strength and weakness of the firm can potentially affect firm value. However, there is insufficient evidence that online employee reviews are valid to predict firm value because the data is relatively difficult to obtain. To fill this gap, from 2014 to 2019, we employed 97,216 reviews collected by JobPlanet, an online firm review website in Korea, and developed a machine learning-based predictive model. Among the proposed models, the LSTM-based model showed the highest accuracy at 73.2%, and the MAE showed the lowest error at 0.359. We expect that this study can be a useful case in the field of firm value prediction on domestic companies.

• 한국벤처창업학회:학술대회논문집
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• 2021.11a
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• pp.185-187
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• 2021

관리종목 지정 제도는 상장 기업 내 기업의 부실화를 경고하여 기업에게는 회생 기회를 주고, 투자자들에게는 투자 위험을 경고하기 위한 시장규제 제도이다. 본 연구는 관리종목과 비관리종목의 기업의 재무 데이터를 표본으로 하여 관리종목 지정 예측에 대한 연구를 진행하였다. 분석에 쓰인 분석 방법은 로지스틱 회귀분석, 의사결정나무, 서포트 벡터 머신, 소프트 보팅, 랜덤 포레스트, LightGBM이며 분류 정확도가 82.73%인 LightGBM이 가장 우수한 예측 모형이었으며 분류 정확도가 가장 낮은 예측 모형은 정확도가 71.94%인 의사결정나무였다. 대체적으로 앙상블을 이용한 학습 모형이 단일 학습 모형보다 예측 성능이 높았다.

• Journal of Korea Technology Innovation Society
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• v.14 no.spc
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• pp.1187-1208
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• 2011

This research aims to propose a systematic approach to identify emerging technology-product portfolio for small and medium enterprises (SMEs). Firstly, operational definition of emerging technology for SMEs is presented. Secondly, research framework is suggested and case study to show usefulness of the newly proposed framwork is analyzed. In detail, reference patent set which represent company's capabilities and business area are constructed. The research constructs patent data set for bibliometric analysis using reference patent set and citing patents to 2nd level. Clustering (expert judgement) and keyword based bibliometric approach are used. Then, cluster activity index (AI) and relevance index (RI) comparing with reference patent set are estimated. With emerging technology-product portfolio using AI and RI, a firm can identify emerging technology-product area and monitoring area.

• Annual Conference of KIPS
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• 2023.11a
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• pp.495-497
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• 2023

본 연구는 기업 간 협력과 경쟁력 강화를 위한 특허 기반 유사 기업 추천 모델을 제안한다. 제안 모델은 특허 데이터와 한국표준산업분류(KSIC) 정보를 활용하여, 특허 정보를 기반으로 기업 간 유사성을 평가하고 유사한 기업을 추천한다. 제안 모델은 특허 초록 정보와 한국표준산업분류를 사용하여 기술 측면에서 기업별 특성을 고려한 기업 대표 벡터를 생성한다. 또한, 기업의 특허 수를 고려하여 정확한 유사기업 추천을 제공합니다. 제안 모델은 기업들이 협력 파트너를 찾고 새로운 비즈니스 기회를 모색하는 데에 도움을 줄 수 있으며, 현재는 NTIS(www.ntis.go.kr)의 분류기반 특허분석 서비스에서 사용 중이다.

• Journal of the Korea Convergence Society
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• v.11 no.8
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• pp.165-171
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• 2020

Most of the stock price prediction research using text mining uses news and SNS data. However, there is a weakness that it is difficult to get honest and vivid information about companies from them. This paper deals with the problem of the prediction for the direction of stock price by doing text mining the online company reviews of internal staff indicating employee satisfaction. The comparative analysis of the prediction models for the direction of stock price showed the prediction model, which adds internal employee reviews, has better performance than those that did not. This paper presents the convergence study using natural language processing in financial engineering. In the field of stock price prediction, This paper pursued a new methodology that used employee satisfaction. In practice, it is expected to provide useful information in the field of forecasting stock price direction.

• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.235-242
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• 2024

This study analyzes the development process of Environmental, Social, and Governance (ESG) policies in Korea and corporate response strategies, comparing them with cases from major countries. The results show that while Korea has established a basic framework for ESG policies through the K-ESG guidelines and plans for mandatory ESG disclosure, these policies lack the specificity and enforceability seen in major countries. In terms of corporate response, large companies are actively formulating ESG strategies, but strengthening ESG capabilities of small and medium-sized enterprises (SMEs) remains an urgent task. Industry-specific ESG strategies reflect the characteristics of each sector, such as carbon neutrality in manufacturing, expansion of responsible investment in finance, and enhanced data security in IT. This study suggests improving Korean ESG policies by enhancing the alignment of ESG disclosure standards with international norms, strengthening tailored support for SMEs, and developing industry-specific policies. For effective corporate ESG response, the study proposes strategic integration of ESG, enhanced communication with stakeholders, and improved ESG data management capabilities.

• The KIPS Transactions:PartD
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• v.15D no.2
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• pp.285-296
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• 2008

The purpose of this paper is to examine the IT governance decision structure of the four strategic modes of IT strategy grid, and compare and analyze the differences in the IT governance decision structure of companies that produce superior results and those that produce inferior results. The survey method was used for this paper, and data from a total of 209 companies that were listed on the KOSDAQ 300 and KOSPI 200 were used for the analysis. The results show that each mode has a different IT governance decision structure from the others, and the IT governance decision structure of companies with high results and those with low results are also different for each mode. The results of this paper are significant in that, for each mode, it presents the decision structure framework for promoting desirable behavior of companies carrying out IT governance.