• 한국지능시스템학회:학술대회논문집
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• 한국지능시스템학회 2008년도 춘계학술대회 학술발표회 논문집
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• pp.89-91
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• 2008

엄청나게 쏟아져 나오는 데이터 홍수 속에서 오늘날의 업체와 연구기관에서는 신속하게 의사 결정을 해야 한다. 당면한 문제점들을 해결하기 위하여 접근할 수 있는 수많은 다양한 데이터 속에서 정확하게 경향을 파악하고 그 근본 원인을 찾아내어 신속하고 action을 행하는 것은 어떠한 회사에서도 성공에 있어서 가장 중요한 인자들 중의 하나이다. 초기 아이디어 도출, 연구 개발에서부터 제품의 생산, 판매 및 서비스에 이르기까지 모든 팀원들은 아주 빠르게 고도의 정확성으로 중요한 결정을 할 필요가 있다. 오늘날의 경쟁 시장에서 기업의 성공은 다른 경쟁자들보다 더 빠르게 결정을 할 수 있는 능력에 달려 있다. 이에 Sporfire에서는 사용자가 쉽고 빠르게 데이터를 분석하여 의사 결정을 할 수 있도록 다양한 기능을 제공하고 있다. 사용자가 SQL같은 전문 언어를 사용하지 않고도 다양한 데이터 source에서 쉽게 데이터를 가져오도록 Information Library를 이용할 수 있으며, 데이터베이스에 들어 있는 숫자들의 집합체를 다양한 차트와 도표들을 이용, 그래픽 적으로 제공해 줌으로써 데이터에 대하여 직관적으로 파악하여 신속하게 대응할 수 있도록 도와준다. 또한 그 결과물들을 MS 파워포인트, 엑셀시트, xml 등으로 저장하여 다른 용도로 사용할 수 있도록 하고 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권8호
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• pp.4090-4102
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• 2018

After emerging online communications, text mining and sentiment analysis has been frequently applied into analyzing electronic word-of-mouth. This study aims to develop a domain-specific lexicon of sentiment analysis to predict box office success in Korea film market and validate the feasibility of the lexicon. Natural language processing, a machine learning algorithm, and a lexicon-based sentiment classification method are employed. To create a movie domain sentiment lexicon, 233,631 reviews of 147 movies with popularity ratings is collected by a XML crawling package in R program. We accomplished 81.69% accuracy in sentiment classification by the Korean sentiment dictionary including 706 negative words and 617 positive words. The result showed a stronger positive relationship with box office success and consumers' sentiment as well as a significant positive effect in the linear regression for the predicting model. In addition, it reveals emotion in the user-generated content can be a more accurate clue to predict business success.

• International Journal of Internet, Broadcasting and Communication
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• 제11권3호
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• pp.41-48
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• 2019

The Management Discussion and Analysis(MD&A) provides investors with an opportunity to gain insight into the company from a manager's perspective and enables short-term and long-term analysis of the business. And MD&A is an important channel through which companies and investors can communicate, providing a useful source of information for analyzing financialstatements. MD&A is measured by the quality of disclosure and there are many previous studies on the usefulness of disclosure information. Therefore, it is very important for the financial analyst who is the representative information user group in the capital market that MD&A Disclosure Quality is measured in real-time in combination with IT information technology and provided timely to financial analyst. In this study, we propose a method that real-time data is converted to digitalized data by combining MD&A disclosure with IT information technology and provided to financial analyst's information environment in real-time. The real-time information provided by MD&A can help the financial analysts' activities and reduce information asymmetry.

• 한국운동역학회지
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• 제31권2호
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• pp.87-94
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• 2021

Objective: Identification of meaningful patterns and trends in large volumes of unstructured data is an important task in various research areas. In the present study, we gathered golf swing image data and did quantitative analysis of swing image. Method: We collected golf swing images of 30 novice players and 30 professional players in this study. Results: We selected important features of swing posture and employed data mining algorithm to classify whether a player is an expert or a novice. Moreover, our proposed method could offer quantitative advices for golf beginners for correcting their swing. Conclusion: Finally, we found a possibility that our proposed method can be expanded to golf swing correction system

• 한국산업융합학회 논문집
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• 제25권4_2호
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• pp.645-653
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• 2022

Recently, deep reinforcement learning has been applied to a variety of industries, such as games, robotics, autonomous vehicles, and data cooling systems. An algorithm called reinforcement learning allows for automated asset allocation without the requirement for ongoing monitoring. It is free to choose its own policies. The purpose of this paper is to carry out an empirical analysis of the performance of asset allocation strategies. Among the strategies considered were the conventional Mean- Variance Optimization (MVO) and the Proximal Policy Optimization (PPO). According to the findings, the PPO outperformed both its benchmark index and the MVO. This paper demonstrates how dynamic asset allocation can benefit from the development of a reinforcement learning algorithm.

• 한국산업융합학회 논문집
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• 제27권2_2호
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• pp.467-473
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• 2024

Metaheuristics' impact is profound across many fields, yet domestic financial portfolio optimization research falls short, particularly in asset allocation. This study delves into metaheuristics for portfolio optimization, examining theoretical and practical benefits. Findings indicate portfolios optimized via metaheuristics outperform the Dow Jones Index in Sharpe ratios, underscoring their potential to enhance risk-adjusted returns significantly. Tabu search, in comparison to Simulated Annealing, demonstrates superior performance by efficiently navigating the search space. Despite these advancements, practical application remains challenging due to the complexities in metaheuristic implementation. The study advocates for broader algorithmic exploration, including population-based metaheuristics, to refine asset allocation strategies further. This research marks a step towards optimizing portfolios from an extensive array of financial assets, aiming for maximum efficacy in investment outcomes.

• Asia pacific journal of information systems
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• 제29권4호
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• pp.717-730
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• 2019

The era of digital transformation (or the fourth industrial revolution) has been triggered by the rapid development of software (SW) technologies. In this era, several studies suspected rapid changes in job structures occurring around the world. Thus, there is a growing need for acquiring the skill sets required for the future. However, there are no specific studies on how existing jobs are changing. To cope with this ambiguity of job changes, this paper aims to investigate how the current job structure is changing in response to digital transformation. To identify the dynamic nature of job change over time, we conducted an analysis based on job posting data. As a result, nine job occupations and fifteen jobs were found.

• The Journal of Asian Finance, Economics and Business
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• 제7권6호
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• pp.265-275
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• 2020

This study investigates the impact of board incentives as proxied by directors' remuneration on the financial performance of listed textile companies in Bangladesh. Using Generalized Method of Moments (GMM) and data pertaining to listed textile companies of Dhaka Stock Exchange (DSE) during the period from 2011 to 2017 (resulting in a total of 140 firm-year observations), we have estimated the firm performance equation involving directors' remuneration and board independence as the independent variables and some other control variables like firm age, size, leverage, and operating efficiency. The results reveal that there is a negative association between board remuneration and firm performance. In addition, this study finds no significant relationship between board independence and firm performance of the sample firms. Our findings suggest that higher pay to the board does not stimulate higher firm performance and, in turn, results in shareholders getting nothing in return from this and, hence, is a matter of great concern for them. Moreover, our results indirectly indicate that currently directors' remuneration in Bangladesh is not aligned with the firm performance, which has been emphasized in extant corporate governance literature. Besides, this paper further raises questions about the effectiveness of independent directors in the boards of textile firms in Bangladesh.

• 인터넷정보학회논문지
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• 제19권1호
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• pp.155-166
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• 2018

These days, online media, such as blogospheres, online communities, and social networking sites, provides the uncountable user-generated content (UGC) to discover market intelligence and business insight with. The business has been interested in consumers, and constantly requires the approach to identify consumers' opinions and competitive advantage in the competing market. Analyzing consumers' opinion about oneself and rivals can help decision makers to gain in-depth and fine-grained understanding on the human and social behavioral dynamics underlying the competition. In order to accomplish the comparison study for rival products and companies, we attempted to do competitive analysis using text mining with online UGC for two popular and competing ramens, a market leader and a market follower, in the Korean instant noodle market. Furthermore, to overcome the lack of the Korean sentiment lexicon, we developed the domain specific sentiment dictionary of Korean texts. We gathered 19,386 pieces of blogs and forum messages, developed the Korean sentiment dictionary, and defined the taxonomy for categorization. In the context of our study, we employed sentiment analysis to present consumers' opinion and statistical analysis to demonstrate the differences between the competitors. Our results show that the sentiment portrayed by the text mining clearly differentiate the two rival noodles and convincingly confirm that one is a market leader and the other is a follower. In this regard, we expect this comparison can help business decision makers to understand rich in-depth competitive intelligence hidden in the social media.

• 지식경영연구
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• 제19권4호
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• pp.59-76
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• 2018

With the arrival of the big data era, customer data and data mining analysis have gradually dominated the process of Customer Relationship Management (CRM). This phenomenon indicates that customer data along with the use of information techniques (IT) have become the basis for building a successful CRM strategy. However, some companies can not discover valuable information through a large amount of customer data, which leads to the failure of making appropriate business strategy. Without suitable strategies, the companies may lose the competitive advantage or probably go bankrupt. The purpose of this study is to propose CRM strategies by segmenting customers into VIPs and Non-VIPs and identifying purchase patterns using the the VIPs' transaction data and data mining techniques (K-means clustering and association rules) of online shopping mall in Korea. The results of this paper indicate that 227 customers were segmented into VIPs among 1866 customers. And according to 51,080 transactions data of VIPs, home product and women wear are frequently associated with food, which means that the purchase of home product or women wears mainly affect the purchase of food. Therefore, marketing managers of shopping mall should consider these shopping patterns when they build CRM strategy.