• The Journal of Information Systems
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• v.29 no.1
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• pp.225-240
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• 2020

Purpose The purpose of this study is to identify features of Gapjil and platform tyranny through South Korea's Gapjil and platform tyranny cases and to suggest countermeasures to both kinds of cases and follow-up study subjects. Methodology/approach We examined South Korea's Gapjil and platform tyranny cases by using Big Data analytics. Then we made a close examination of the two typical cases, through which we compared features and countermeasures of Gapjil and those of platform tyranny. Findings Gapjil mostly occurred at conventional companies and franchise companies, between major and minor companies, or due to lack of owner's qualifications. The features of platform tyranny were excessively monopolistic structure of platform business, inadequate legal sanctions, and features of ICT companies. Establishment of legal bases for sanctions and education for platform participants were suggested as countermeasures.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.855-857
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• 2019

개인 정보의 확산 및 유출의 문제점으로 인해 보이스 피싱의 피해 건수가 증가하고 있다. 이러한 보이스 피싱의 사회적 문제에 대하여 상황 인지 및 사용자 의도 파악 시스템을 적용하여 해결책으로 제안하고자 한다. 이 시스템은 음성 전화로 이루어지는 순차 정보를 텍스트 데이터에 기반하여 사기범의 문맥적 흐름에서 행위 동사를 추출한다. 추출된 행위 동사의 순차 정보를 통해 보이스 피싱의 상황임을 인지하고, 흐름의 행동 패턴을 분석하여 사기범의 의도를 파악한다. 이러한 상황 인지 및 사용자 의도 파악 시스템은 개인 정보의 문제뿐만 아니라 경제적 피해 규모를 축소시킬 것으로 예상된다.

• International journal of advanced smart convergence
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• v.8 no.3
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• pp.193-200
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• 2019

Accruals-Quality(AQ) is an important proxy for evaluating the quality of accounting information disclosures. High-quality accounting information will provide high predictability and precision in the disclosure of earnings and will increase the response to stock prices. And high Accruals-Quality, such as mitigating heterogeneity in accounting information interpretation, provides information usefulness in capital markets. The purpose of this study is to suggest how AQ, which represents the quality of accounting information disclosure, is transformed into digitized data in real-time in combination with IT information technology and provided to financial analyst's information environment in real-time. And AQ is a framework for predictive analysis through big data log analysis system. This real-time information from AQ will help financial analysts to increase their activity and reduce information asymmetry. In addition, AQ, which is provided in real time through IT information technology, can be used as an important basis for decision-making by users of capital market information, and is expected to contribute in providing companies with incentives to voluntarily improve the quality of accounting information disclosure.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.290-296
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• 2022

Big Data is a data analysis technology empowered by late advances in innovations and engineering. In any case, big data involves a colossal responsibility of equipment and handling assets, making reception expenses of big data innovation restrictive to little and medium estimated organizations. Cloud computing offers the guarantee of big data execution to little and medium measured organizations. Big Data preparing is performed through a programming worldview known as MapReduce. Normally, execution of the MapReduce worldview requires organized joined stockpiling and equal preparing. The computing needs of MapReduce writing computer programs are frequently past what little and medium measured business can submit. Cloud computing is on-request network admittance to computing assets, given by an external element. Normal arrangement models for cloud computing incorporate platform as a service (PaaS), software as a service (SaaS), framework as a service (IaaS), and equipment as a service (HaaS).

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.192-198
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• 2022

Companies are building and utilizing their own data analysis systems according to business characteristics in the distributed cloud. However, as businesses and data types become more complex and diverse, the demand for more efficient analytics has increased. In response to these demands, in this paper, we propose an unsupervised learning-based data analysis agent to which reinforcement learning is applied for effective data analysis. The proposal agent consists of reinforcement learning processing manager and unsupervised learning manager modules. These two modules configure an agent with k-means clustering on multiple nodes and then perform distributed training on multiple data sets. This enables data analysis in a relatively short time compared to conventional systems that perform analysis of large-scale data in one batch.

• Cartoon and Animation Studies
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• s.36
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• pp.553-572
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• 2014

In this paper, for the evaluation of the ease of a particular Web site (www.appbelt.net), insert the log tracking code for Google Analytics in a page of the Web site to collect behavioral data of visitor and has studied the improvement measures for the problems of the Web site, after the evaluation of the overall quality of the Web site through the evaluation of Coolcheck. These findings set the target value of the company's priority (importance) companies want to influence the direction of the business judgment are set up correctly, and the user's needs and behavior will be appropriate for the service seems to help improvement.

• Information Systems Review
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• v.24 no.1
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• pp.157-181
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• 2022

As competition in the airline industry intensifies, effective airline service quality evaluation has become one of the main challenges. In particular, as big data analytics has been touted as a new research paradigm, new research on service quality measurement using online review analysis has been attempted. However, these studies do not use review titles for analysis, relyon supervised learning that requires a lot of human intervention in learning, and do not consider airline characteristics in classifying service quality dimensions.To overcome the limitations of existing studies, this study attempts to measure airlines service quality and to classify it into the AIRQUAL service quality dimension using online review text as well as title based on self-trainingand sentiment analysis. The results show the way of effective extracting service quality dimensions of AIRQUAL from online reviews, and find that each service quality dimension have a significant effect on service satisfaction. Furthermore, the effect of review title on service satisfaction is also found to be significant. This study sheds new light on service quality measurement in airline industry by using an advanced analytical approach to analyze effects of service quality on customer satisfaction. This study also helps managers who want to improve customer satisfaction by providing high quality service in airline industry.

• Information Systems Review
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• v.23 no.1
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• pp.155-172
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• 2021

With the recent increase in online product purchases, a recommender system that recommends products considering users' preferences has still been studied. The recommender system provides personalized product recommendation services to users. Collaborative Filtering (CF) using user ratings on products is one of the most widely used recommendation algorithms. During CF, the item-based method identifies the user's product by using ratings left on the product purchased by the user and obtains the similarity between the purchased product and the unpurchased product. CF takes a lot of time to calculate the similarity between products. In particular, it takes more time when using text-based big data such as review data of Amazon store. This paper suggests a hybrid recommendation system using a 2-phase methodology and text data mining to calculate the similarity between products easily and quickly. To this end, we collected about 980,000 online consumer ratings and review data from the online commerce store, Amazon Kinder Store. As a result of several experiments, it was confirmed that the suggested hybrid recommendation system reflecting the user's rating and review data has resulted in similar recommendation time, but higher accuracy compared to the CF-based benchmark recommender systems. Therefore, the suggested system is expected to increase the user's satisfaction and increase its sales.

• Information Systems Review
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• v.23 no.3
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• pp.51-75
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• 2021

Global e-commerce websites offer personalized recommendation services to gain sustainable competitiveness. Existing studies have offered personalized recommendation services using quantitative preferences such as ratings. However, offering personalized recommendation services using only quantitative data has raised the problem of decreasing recommendation performance. For example, a user gave a five-star rating but wrote a review that the user was unsatisfied with hotel service and cleanliness. In such cases, has problems where quantitative and qualitative preferences are inconsistent. Recently, a growing number of studies have considered review data simultaneously to improve the limitations of existing personalized recommendation service studies. Therefore, in this study, we identify review and rating mismatches and build a new user profile to offer personalized recommendation services. To this end, we use deep learning algorithms such as CNN, LSTM, CNN + LSTM, which have been widely used in sentiment analysis studies. And extract sentiment features from reviews and compare with quantitative preferences. To evaluate the performance of the proposed methodology in this study, we collect user preference information using real-world hotel data from the world's largest travel platform TripAdvisor. Experiments show that the proposed methodology in this study outperforms the existing other methodologies, using only existing quantitative preferences.

• Journal of Convergence for Information Technology
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• v.10 no.9
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• pp.8-14
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• 2020

The objective of this research is to investigate how implementation of smart factory systems(SFS) effects management accounting(MA). The results based on data collected from 108 Korea small and medium enterprises(SME) confirmed that SFS implementation caused significant MA changes. Estimated regression models revealed that the most important SFS characteristic were the analytical capabilities since it positively influenced MA changes in four dimensions: internal reporting, budgeting, application of modern accounting techniques and MA employee's job. In the segment of budgeting, the quality of implementation of specialized bedgeting software had significant and positive influence. The only negative correlation founded was the one between the uncertainty of business environment and adoption of modern accounting techniques. Results from this study provide that SME should put special focus on implementation of business analytics modules in order to achieve comprehensive benefits in MA prctices.