• Information Systems Review
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• v.20 no.1
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• pp.81-98
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• 2018

ERP supports the automation and integration of business processes of enterprises and records voluminous data about the business activities of enterprises. The academe and business enterprises are focusing on process mining, which improves the performance of business processes and strengthens compliance. However, these studies focused on analysis of the business process of large companies, which adopts foreign ERP, such as SAP ERP or Oracle ERP. In comparison with foreign ERP, domestic ERP lags behind in terms of logging and managing of event data. Therefore, the application of process mining to domestic ERP is a challenging task. The present study aims to analyze domestic ERP based on process mining to overcome this challenge. This study discusses the lessons learned from a case study in a Korean cosmetics manufacturing company. Our results are expected to strengthen the competitiveness of Korean small and medium-sized enterprises that adopt domestic ERP and realize the outcomes of the large investment of the Korean government on the ERP implementation of enterprises.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.800-806
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• 2007

IT 벤처기업은 자본 대비 높은 수익성을 가지므로 지식기반 산업환경에서 많은 투자자들의 집중적인 관심을 받고 있다. 이러한 IT 벤처기업의 효율성을 평가하기 위한 방안으로, DEA 와 데이터마이닝 기법을 통합하는 방안을 제시하였다. 국내 코스닥 상장 기업 가운데 IT 에 주력하고 있는 벤처기업들을 대상으로 본 연구에서 제시한 효율성 평가방법을 적용 하였다. 대표적인 비모수적 분석기법인 Data Envelopment Analysis(DEA)를 이용하여 연구대상 기업들을 효율기업 및 비효율기업으로 구분한 후, DEA 의 효율성을 설명하는 모형을 logit 을 이용하여 구축하였다. DEA 는 기업의 상대적인 효율성을 측정하는 데에서 우수하지만, 효율성 정도를 설명하는 모형의 구축에는 한계가 있다. 이를 보완한 DEA 의 결과를 logit 과 통합한 효율성 모형에 대해서 데이터 마이닝 기법인 logit, 판별분석, Support Vector Machine(SVM) 등을 적용하여 IT 벤처기업의 효율성을 사전에 예측하여 평가 및 투자에 활용할 수 있는 방안을 제시하였다.

• Journal of Korea Technology Innovation Society
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• v.13 no.3
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• pp.513-537
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• 2010

According to the "FDI-as-market-discipline" hypothesis, inward FDI acts as a mechanism of change in market structure affecting innovative activities of domestic firms. We used panel KIS data for testing this hypothesis. Binary probit estimation shows that, in contrast to the German case of Bertschek (1995), FDI is insignificant in Korean case for explaining product innovation. 1his result maybe comes from the fact that the industries in Korea are more monopolistic or oligopolistic than those of Germany. Using panel data, we tried random parameter estimation using matrix weighted average of GLS and OLS. The result shows different estimates from cross-section outcome and panel estimation with parameter homogeneity, so we can infer large parameter heterogeneity across firms. But, interpretation for FDI variable is similar across panel and cross-section estimation.

• Journal of Convergence for Information Technology
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• v.7 no.5
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• pp.177-182
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• 2017

As major ICT technologies such as IoT, cloud computing, and Big Data are being applied to manufacturing, smart factories are beginning to be built. The key of smart factory implementation is the ability to acquire and analyze data of the factory. Therefore, the need for a big data analysis platform is increasing. The purpose of this study is to construct a platform for big data analysis of manufacturing process and propose integrated method for analysis. The proposed platform is a RHadoop-based structure that integrates analysis tool R and Hadoop to distribute a large amount of datasets. It can store and analyze big data collected in the unit process and factory in the automation system directly in HBase, and it has overcome the limitations of RDB - based analysis. Such a platform should be developed in consideration of the unit process suitability for smart factories, and it is expected to be a guide to building IoT platforms for SMEs that intend to introduce smart factories into the manufacturing process.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.755-764
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• 2011

In many companies field failure data is used to predict the future number of failures, especially when an unexpected failure mode happens to be a problem. It is because they want to predict the number of spare parts needed and the future quality warranty cost associated with the part based on the predictions of the future number of failures. In this paper field summary data is used to predict the future number of failures based on an appropriate distribution. Other types of data are also investigated to identify the appropriate distribution.

• Journal of Digital Contents Society
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• v.15 no.2
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• pp.219-225
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• 2014

Competition between corporations is getting more intense, so they need customer feedback in order to fulfill an effective management. Recently, SNS (Social Network Service) such as Twitter and Facebook has grown dramatically because of smart phones. Social media like Twitter and Facebook let customers to express their needs, and using big data such as data on SNS is a very effective method for getting customer's feedback. Collecting and analyzing social big data are operated by Buzz monitoring system. This research suggests how to utilize big data for getting customer's feedback on hotel CRM(Customer Relationship Management), which considers customer itself as asset of business. This paper demonstrates the research of buzz monitoring system that analyzes big data, and presents results of hotel customer reputation using buzz monitoring system. It would analyze the result from the hotel customer reputation, and research the implication in this paper.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.01a
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• pp.321-324
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• 2015

본 논문에서는 SNS에 존재하는 특정 제품과 브랜드 또는 기업에 대한 평가, 의견, 느낌, 사용 후기 등의 소비자 생각을 수집하여 기업에서 향후 신제품 개발이나 시장 진출 및 확대 등의 경영활동에 활용할 수 있도록 SNS 빅데이터를 문석하고, 이를 활용하여 보다 소집단화 되고 개인화 되어가는 Micro-Trend 중심의 마케팅 활동을 할 수 있는 Micro-Targeting 관련 분석 정보를 제공 모델링하는 것을 제안한다. 본 연구에서는 SNS 데이터의 수집, 저장, 분석에 대한 내용을 다루고 있으며, 특히 마이크로타겟팅을 위한 정보를 머하웃(Mahout)의 유클리드 거리 기반의 유사도와 K-평균 군집 알고리즘을 활용하여 구현하고자 하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.01a
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• pp.457-460
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• 2014

본 논문에서는 네트워크의 발전과 빅 데이터 등의 차세대 기술에 발맞추어 단방향적 정보전달이 아닌, 고객 중심의 고객만족 서비스를 제안하고자 하나의 앱을 제작한다. 뷰티 캐스터 앱은 사용자들의 기본정보, 환경정보, 외부정보를 통합 분석한 '개인별 뷰티지수' 산출 등의 개인 정보 서비스와 함께 개인별 뷰티지수를 활용한 'Auto-counselling' 제공하고, 사용자의 정보로부터 획득한 히스토리를 분석하여 맞춤형 Commerce를 구성한다. 이러한 뷰티 캐스터 앱은 실제 코스메틱 기업에서 활용 가능하여 어플리케이션 상용화 시, 정보를 제공하는 기업과 기능을 제공하는 뷰티 캐스터 모두 이익을 얻을 수 있을 것이며, 이러한 뷰티 캐스터는 빅 데이터를 기반으로한 사용자의 니즈 파악이 빠른 어플리케이션 이므로 사용자들의 구매에 따라 컨텐츠가 변화하기 때문에 고객들이 원하는 정보를 빠르게 습득하여 전달 할 수 있다. 또한, 뷰티 지수의 고도화를 통한 '대한민국 코스메틱 지표화'를 기대하여 그 지표를 통해 고객들의 코스메틱 구매 기준을 마련할 수 있다.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.37-51
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• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.