• Journal of Internet Computing and Services
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• v.17 no.4
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• pp.69-77
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• 2016

Recently, the importance of visual analytics has been recognized in the field of business intelligence. From the view of business intelligence, visual analytics aims for acquiring valuable insights for decision making by interactively visualizing a variety of business information. In this paper, we propose a treemap-based method for visualizing work-sharing relationships among business process performers. A work-sharing relationship is established between two performers who jointly participate in a specific activity of a business process and is an important factor for understanding organizational structures and behaviors in a process-centric organization. To this end, we design and implement a treemap-based visualization tool for representing work-sharing relationships as well as basic hierarchical information in business processes. Finally, we evaluate usefulness of the proposed visualization tool through an operational example using XPDL (XML Process Definition Language) process models.

• 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.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.249-265
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• 2023

Recently, the satellite industry has been paying attention to the private-led 'New Space' paradigm, which is a departure from the traditional government-led industry. The space industry, which is considered to be the next food industry, is still receiving relatively little attention in Korea compared to the global market. Therefore, the purpose of this study is to explore future signals that can help determine the market entry strategies of private companies in the domestic satellite industry. To this end, this study utilizes the theoretical background of future signal theory and the Keyword Portfolio Map method to analyze keyword potential in patent document data based on keyword growth rate and keyword occurrence frequency. In addition, news data was collected to categorize future signals into first symptom and early information, respectively. This is utilized as an interpretive indicator of how the keywords reveal their actual potential outside of patent documents. This study describes the process of data collection and analysis to explore future signals and traces the evolution of each keyword in the collected documents from a weak signal to a strong signal by specifically visualizing how it can be used through the visualization of keyword maps. The process of this research can contribute to the methodological contribution and expansion of the scope of existing research on future signals, and the results can contribute to the establishment of new industry planning and research directions in the satellite industry.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.1-7
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• 2022

With the development of artificial intelligence, efforts to incorporate neuroscience mining with AI have increased. Neuroscience mining, also known as NSM, expands on this concept by combining computational neuroscience and business analytics. Using fNIRS (functional near-infrared spectroscopy)-based experiment dataset, we have investigated the potential of NSM in the context of the BPSC (business problem-solving creativity) prediction. Although BPSC is regarded as an essential business differentiator and a difficult cognitive resource to imitate, measuring it is a challenging task. In the context of NSM, appropriate methods for assessing and predicting BPSC are still in their infancy. In this sense, we propose a novel NSM method that systematically combines CNN, BiLSTM, and attention network for the sake of enhancing the BPSC prediction performance significantly. We utilized a dataset containing over 150 thousand fNIRS-measured data points to evaluate the validity of our proposed NSM method. Empirical evidence demonstrates that the proposed NSM method reveals the most robust performance when compared to benchmarking methods.

• Knowledge Management Research
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• v.22 no.3
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• pp.31-53
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• 2021

As a communication channel for individuals, social media is affecting various areas such as business, economy, politics, and society. One of the less-studied areas is the law. Therefore, this study collected various information from social media and analyzed its impacts on the legal decisions, especially the Supreme Court decisions in Korea. This study was conducted by compiling information from Internet news articles and public responses. We found that when the negative reactions from the public got higher, the trial duration until the supreme court making the final decisions became shorter. However, we were not able to find the significant relationship between social media reactions and dismissal of appeal nor annulment. Our study would contribute to the information systems and knowledge management research in a sense that the social analytics is applied to the area of legal decisions, instead of using conventional qualitative study methodology. Our study is also meaningful to the practitioners because that big data analytical business can be applied to the field of law by creating a new database for the emerging legal technology. Finally, law makers can think of a better way to standardize the legal decision process to minimize the reverse effects from social media.

• The Journal of Information Systems
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• v.24 no.4
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• pp.139-152
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• 2015

With the advancement of the Internet/IT technologies and the increased computation power, massive data can be collected, stored, and processed these days. The availability of large databases has brought forth a new era in which companies are hard pressed to find innovative ways to utilize immense amounts of data at their disposal. Indeed, data has opened a new age of business operations and management. There are already many cases of innovative businesses reaping success thanks to scientific decisions based on data analysis and mathematical algorithms. Big Data is a new paradigm in itself. In this article, Big Data is viewed as a new perspective rather than a new technology. This value centric definition of Big Data provides a new insight and opportunities. Moreover, the Business Analytics, which is the framework of creating tangible results in management, is introduced. Then the Internet of Things (IoT), another innovative concept of data collection and networking, is presented and how this new concept can be interpreted with Big Data in terms of the value centric perspective. The challenges and opportunities with these new concepts are also discussed.

• The Journal of Bigdata
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• v.8 no.1
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• pp.61-71
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• 2023

With the development of data-driven decision-making and sophisticated big data processing technique, there is a growing demand for information on how to process data. However, recent studies with data preprocessing mentioned only as a means to achieve a result. Therefore, in this study, we aimed to write in detail about the data processing pipeline, include preprocessing data. In particular, we shares the context and domain knowledge to aid fluent understand of the research.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.155-170
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• 2018

Predicting a company's financial bankruptcy is traditionally one of the most crucial forecasting problems in business analytics. In previous studies, prediction models have been proposed by applying or combining statistical and machine learning-based techniques. In this paper, we propose a novel intelligent prediction model based on the simulated annealing which is one of the well-known optimization techniques. The simulated annealing is known to have comparable optimization performance to the genetic algorithms. Nevertheless, since there has been little research on the prediction and classification of business decision-making problems using the simulated annealing, it is meaningful to confirm the usefulness of the proposed model in business analytics. In this study, we use the combined model of simulated annealing and machine learning to select the input features of the bankruptcy prediction model. Typical types of combining optimization and machine learning techniques are feature selection, feature weighting, and instance selection. This study proposes a combining model for feature selection, which has been studied the most. In order to confirm the superiority of the proposed model in this study, we apply the real-world financial data of the Korean companies and analyze the results. The results show that the predictive accuracy of the proposed model is better than that of the naïve model. Notably, the performance is significantly improved as compared with the traditional decision tree, random forests, artificial neural network, SVM, and logistic regression analysis.

• The Journal of Bigdata
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• v.7 no.2
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• pp.113-128
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• 2022

After COVID-19, interest in "sustainable tourism" increased, and the number of tourists who wanted to experience "sustainable tourism" also increased. However, there is a problem that the programs and methods for 'sustainable tourism' are not specific and diverse. In addition, since most of the interests of "sustainable tourism" focus on "environment" and "carbon neutrality," there are not many programs or government policies that can contribute to the community. Therefore, in this study, news data and review data were analyzed to suggest a method for promoting 'sustainable tourism'. First, in this study, major themes of sustainable travel were derived through news big data analysis. Through this analysis, policy themes and events of 'sustainable tourism' were derived. By analyzing news big data related to "sustainable tourism," we would like to analyze the reasons why sustainable travel has not been activated in Korea. Finally, in order to promote sustainable travel in Jeju island, we analyzed user review data of Jeju local currency, and propose a idea to coexist with the local community.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.365-385
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• 2022

The easy payment service is a payment and remittance service that uses a simple authentication method. As online transactions have increased due to COVID-19, the use of an easy payment service is increasing. At the same time, electronic financial industries such as Naver Pay, Kakao Pay, and Toss are diversifying the competition structure of the easy payment market; meanwhile overseas fintech companies PayPal and Alibaba have a unique market share in their own countries, while competition is intensifying in the domestic easy payment market, as there is no unique market share. In this study, the participants in the easy payment market were classified as electronic financial companies, mobile phone manufacturers, and financial companies, and a SWOT analysis was conducted on the representative services in each industry. The analysis examined the user reviews of Google Play Store via a topic modeling analysis, and it employed positive topics as strengths and negative topics as weaknesses. In addition, topic modeling was conducted by dividing news articles into political, economic, social, and technology (PEST) articles to derive the opportunities and threats to easy payment services. Through this research, we intend to confirm the service capabilities of easy payment companies and propose a service activation strategy that allows gaining the upper hand in the market.