• The Journal of Asian Finance, Economics and Business
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• 제6권3호
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• pp.173-183
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• 2019

This study aims to utilize big data contents of news and social media for developing a corporate strategy of multinational enterprises and their global decision-making through the data mining technique, especially text mining. In this paper, the data of 2 news media (BBC and CNN) and 2 social media (Facebook and Twitter) were collected for the three global leading Korean companies (Samsung, Hyundai Motor Company, and LG) from April, 2018 to April, 2019. The findings of this paper have shown that traditional news media and also modern social media have become devastating tools to extract global trends or phenomena for businesses. Moreover, this presents that a company can adopt a two-track strategy through two different types of media by deriving the key issues or trends from news media channels and also grasping consumers' sentiments, preference or issues of interest such as battery or design from social media. In addition, analyzing the texts of those media and understanding the association rules greatly contribute to the comparison between two different types of media channels to see the difference. Lastly, this provides meaningful and valuable data empowered insights to find a future direction comprehensively and develop a global strategy for sustainability of business.

• 한국컴퓨터정보학회논문지
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• 제29권6호
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• pp.101-112
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• 2024

본 논문에서는 프로세스 마이닝을 위한 이벤트 로그 생성을 지원하도록 설계된 대화형 도구인 EDF(Event Data Factory)를 소개한다. EDF는 다양한 데이터 커넥터를 통합하여 사용자가 다양한 데이터 소스에 연결할 수 있도록 지원한다. 이 도구는 그래프 기반 시각화와 함께 로우 코드/노코드 기술을 사용하여 비전문가 사용자가 프로세스 흐름을 이해하도록 돕고, 사용자 경험을 향상시킨다. EDF는 메타데이터 정보를 활용하여 사용자가 case, activity 및 timestamp 속성을 포함하는 이벤트 로그를 효율적으로 생성할 수 있도록 한다. 로그 품질 메트릭을 통해 사용자는 생성된 이벤트 로그의 품질을 평가할 수 있다. 우리는 클라우드 기반 아키텍처에서 EDF를 구현하고 성능평가를 실행했으며, 본 연구와 결과는 EDF의 사용성과 적용 가능성을 보여주었다. 마지막으로 관찰 연구를 통해 EDF가 사용하기 쉽고 유용하여 프로세스 마이닝 애플리케이션에 대한 중소기업(SME)의 접근을 확장한다는 사실을 확인했다.

• 한국콘텐츠학회논문지
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• 제6권1호
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• pp.179-189
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• 2006

최근 데이터 수집 및 저장기술의 발달, 데이터베이스 관리시스템과 데이터웨어하우스 기술의 광범위한 사용은 기업내부의 대량의 데이터를 축적할 수 있도록 하고 있으며, 축적된 데이터는 의사결정에 필요한 새롭고 가치 있는 정보와 지식을 획득할 수 있는 잠재적인 원천으로 인정되고 있다. 본 논문에서는 이동통신업체의 데이터를 가지고 데이터 마이닝 방법론을 이용하여 기존고객을 세분화하기 위한 예측모델을 설계한다. 이를 통해 고객 개개인의 특성에 맞는 마케팅 프로모션을 하게 하고 신규고객을 획득할 때는 신규 고객의 특성을 미리 예측하여 세분화함으로써 고객의 평생가치를 촉진하여 기업과 고객과의 관계성을 높여서 기업은 안정된 고객층으로부터 수익을 창출하고 고객들은 해당 기업으로부터 더 많은 혜택을 받게 하는데 목적이 있다.

• Journal of information and communication convergence engineering
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• 제22권1호
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• pp.44-55
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• 2024

High-utility itemset mining (HUIM) is a dominant technology that enables enterprises to make real-time decisions, including supply chain management, customer segmentation, and business analytics. However, classical support value-driven Apriori solutions are confined and unable to meet real-time enterprise demands, especially for large amounts of input data. This study introduces a groundbreaking model for top-N high utility itemset mining in real-time enterprise applications. Unlike traditional Apriori-based solutions, the proposed convolutional sequential embedding metrics-driven cosine-similarity-based multilayer perception learning model leverages global and contextual features, including semantic attributes, for enhanced top-N recommendations over sequential transactions. The MATLAB-based simulations of the model on diverse datasets, demonstrated an impressive precision (0.5632), mean absolute error (MAE) (0.7610), hit rate (HR)@K (0.5720), and normalized discounted cumulative gain (NDCG)@K (0.4268). The average MAE across different datasets and latent dimensions was 0.608. Additionally, the model achieved remarkable cumulative accuracy and precision of 97.94% and 97.04% in performance, respectively, surpassing existing state-of-the-art models. This affirms the robustness and effectiveness of the proposed model in real-time enterprise scenarios.

• 한국IT서비스학회지
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• 제14권2호
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• pp.159-176
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• 2015

As a public medium and one of the big data sources that is accumulated informally and real time, online news comments or replies are considered a significant resource to understand mentalities of article readers. The comments are also being regarded as an important medium of WOM (Word of Mouse) about products, services or the enterprises. If the diffusing effect of the comments is referred to as the degrees of agreement and disagreement from an angle of WOM, figuring out which characteristics of the comments would influence the agreements or the disagreements to the comments in very early stage would be very worthwhile to establish a comment-based eWOM (electronic WOM) strategy. However, investigating the effects of the characteristics of the comments on eWOM effect has been rarely studied. According to this angle, this study aims to conduct an empirical analysis which understands the characteristics of comments that affect the numbers of agreement and disagreement, as eWOM performance, to particular news articles which address a specific product, service or enterprise per se. While extant literature has focused on the quantitative attributes of the comments which are collected by manually, this paper used text mining techniques to acquire the qualitative attributes of the comments in an automatic and cost effective manner.

• 지식경영연구
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• 제15권3호
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• pp.169-188
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• 2014

Big data seems to change knowledge management system and method of enterprises to large extent. Further, the type of method for utilization of unstructured data including image, v ideo, sensor data a nd text may determine the decision on expansion of knowledge management of the enterprise or government. This paper, in this light, attempts to figure out the prediction model of demands and supply for big data market of Korea trough data mining decision making tree by utilizing text bit data generated for 3 years on web and SNS for expansion of form for knowledge management. The results indicate that the market focused on H/W and storage leading by the government is big data market of Korea. Further, the demanders of big data have been found to put important on attribute factors including interest, quickness and economics. Meanwhile, innovation and growth have been found to be the attribute factors onto which the supplier puts importance. The results of this research show that the factors affect acceptance of big data technology differ for supplier and demander. This article may provide basic method for study on expansion of analysis form of enterprise and connection with its management activities.

• 한국컴퓨터정보학회논문지
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• 제24권9호
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• pp.119-125
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• 2019

The purpose of this study is to find out the relevant factors of the choice tendency of tourism users to Intelligent Tourism platform through big data analysis, which will help enterprises to make accurate positioning and improvement according to user information feedback in the tourism market in the future, so as to gain the favor of users' choice and achieve long-term market competitiveness. This study takes the Intelligent Tourism platform as the independent variable and the user choice tendency as the dependent variable, and explores the related factors between the Intelligent Tourism platform and the user choice tendency. This study make use of text mining and R language text analysis, and uses SPSS and AMOS statistical analysis tools to carry out empirical analysis. According to the analysis results, the conclusions are as follows: service quality has a significant positive correlation with user choice tendency; service quality has a significant positive correlation with tourism trust; Tourism Trust has a significant positive correlation with user choice tendency; service quality has a significant positive correlation with user experience; user experience has a significant positive correlation with user choice tendency Positive correlation effect.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권8호
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• pp.4108-4122
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• 2019

Workflow process mining is becoming a more and more valuable activity in workflow-supported enterprises, and through which it is possible to achieve the high levels of qualitative business goals in terms of improving the effectiveness and efficiency of the workflow-supported information systems, increasing their operational performances, reducing their completion times with minimizing redundancy times, and saving their managerial costs. One of the critical challenges in the workflow process mining activity is to devise a reasonable approach to discover and recognize the bottleneck points of workflow process models from their enactment event histories. We have intuitively realized the fact that the iterative process pattern of redo-activities ought to have the high possibility of becoming a bottleneck point of a workflow process model. Hence, we, in this paper, propose an algorithmic approach and its implementation to discover the redo-activities and their performers' involvements patterns from workflow process enactment event logs. Additionally, we carry out a series of experimental analyses by applying the implemented algorithm to four datasets of workflow process enactment event logs released from the BPI Challenges. Finally, those discovered redo-activities and their performers' involvements patterns are visualized in a graphical form of information control nets as well as a tabular form of the involvement percentages, respectively.

• 한국인터넷방송통신학회논문지
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• 제14권6호
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• pp.289-294
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• 2014

최근 기업에서 데이터 마이닝 기법을 이용한 CRM을 마케팅이나 새로운 기획에 활용하고 있다. 그러나 데이터 마이닝 기술은 전문지식이 필요하여 일반인의 접근이 어렵고 시간과 공간의 제약을 받게 된다. 본 논문에서는 이를 해결하기 위해 데이터 마이닝 기법을 적용한 Mobile CRM을 제안하였다. 이를 위해 기존 CRM 시스템의 구조를 분석하고 데이터 흐름과 형식을 정의 하였다. 또한 시스템 프로세스를 정의하여 데이터 마이닝 기법을 이용한 판매동향분석 알고리즘과 고객판매추천 알고리즘을 설계하였다. 제안 시스템에 대한 평가는 시나리오 테스트를 통해 정상 동작을 확인하였으며 기존 시스템과의 비교 검증을 실시하였다. 테스트 결과 기존 프로그램과 데이터 값이 일치하여 신뢰성을 확인하고 제안한 통계 테이블 조회를 통해 데이터 분석 시간을 감소시켜 신속성을 검증하였다.

• 산업경영시스템학회지
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• 제44권2호
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• pp.43-57
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• 2021

Smart factories can be defined as intelligent factories that produce products through IoT-based data. In order to build and operate a smart factory, various new technologies such as CPS, IoT, Big Data, and AI are to be introduced and utilized, while the implementation of a MES system that accurately and quickly collects equipment data and production performance is as important as those new technologies. First of all, it is very essential to build a smart factory appropriate to the current status of the company. In this study, what are the essential prerequisite factors for successfully implementing a smart factory was investigated. A case study has been carried out to illustrate the effect of implementing ERP and MES, and to examine the extensibilities into a smart factory. ERP and MES as an integrated manufacturing information system do not imply a smart factory, however, it has been confirmed that ERP and MES are necessary conditions among many factors for developing into a smart factory. Therefore, the stepwise implementation of intelligent MES through the expansion of MES function was suggested. An intelligent MES that is capable of making various decisions has been investigated as a prototyping system by applying data mining techniques and big data analysis. In the end, in order for small and medium enterprises to implement a low-cost, high-efficiency smart factory, the level and goal of the smart factory must be clearly defined, and the transition to ERP and MES-based intelligent factories could be a potential alternative.