• Journal of Korean Institute of Industrial Engineers
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• v.43 no.1
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• pp.62-71
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• 2017

In order to attract foreign tourists, it is important to understand what factors on domestic tour spots are critically considered and how they are evaluated after visit. However, most of the researches on tour business have collected information from tourists through survey on a small number of tourists, which leads to inaccurate and biased conclusion. In this paper, we suggest a data-driven methodology to figure out tourists' satisfaction factors and estimate sentiment scores on them. To do so, we collected review comments data from popular web site. Latent dirichlet allocation is employed to extract key factors and elastic net is used to estimate sentiment scores. Then, an aggregated evaluation score is generated by combining the factors and the sentiment scores per topics. Our proposed method can be used to recommend travel schedules with themes and discover new spots.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.4133-4145
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• 2017

Advances in science and technology are driving us to the better life but also forcing us to make more investment at the same time. Therefore, the government has provided the investment to carry on the promising futuristic technology successfully. Indeed, a lot of resources from the government have supported into the science and technology R&D projects for several decades. However, the performance of the public investments remains unclear in many ways, so thus it is required that planning and evaluation about the new investment should be on data driven decision with fact based evidence. In this regard, the government wanted to know the trend and issue of the science and technology with evidences, and has accumulated an amount of database about the science and technology such as research papers, patents, project reports, and R&D information. Nowadays, the database is supporting to various activities such as planning policy, budget allocation, and investment evaluation for the science and technology but the information quality is not reached to the expectation because of limitations of text mining to drill out the information from the unstructured data like the reports and papers. To solve the problem, this study proposes a practical text mining methodology for the science and technology trend analysis, in case of aerospace technology, and conduct text mining methods such as ontology development, topic analysis, network analysis and their visualization.

• The korean journal of orthodontics
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• v.41 no.5
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• pp.354-360
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• 2011

Objective: To investigate the effects of different pilot-drilling methods on the biomechanical stability of self-tapping mini-implant systems at the time of placement in and removal from artificial bone blocks. Methods: Two types of artificial bone blocks (2-mm and 4-mm, 102-pounds per cubic foot [102-PCF] polyurethane foam layered over 100-mm, 40-PCF polyurethane foam) were custom-fabricated. Eight mini-implants were placed using the conventional motor-driven pilot-drilling method and another 8 mini-implants were placed using a novel manual pilot-drilling method (using a manual drill) within each of the 2-mm and 4-mm layered blocks. The maximum torque values at insertion and removal of the mini-implants were measured, and the total energy was calculated. The data were statistically analyzed using linear regression analysis. Results: The maximum insertion torque was similar regardless of block thickness or pilot-drilling method. Regardless of the pilot-drilling method, the maximum removal torque for the 4-mm block was statistically higher than that for the 2-mm block. For a given block, the total energy at both insertion and removal of the mini-implant for the manual pilot-drilling method were statistically higher than those for the motor-driven pilot-drilling method. Further, the total energies at removal for the 2-mm block was higher than that for the 4-mm block, but the energies at insertion were not influenced by the type of bone blocks. Conclusions: During the insertion and removal of mini-implants in artificial bone blocks, the effect of the manual pilot-drilling method on energy usage was similar to that of the conventional, motor-driven pilot-drilling method.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.64-69
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• 2024

Blockchain is a decentralized network in which data blocks are linked. Through a decentralized peer-to-peer network, users can create shared databases, resulting in a trustworthy and aggregated database known as a blockchain that enhances reliability and security. The distributed nature of the blockchain enables data to be stored on multiple nodes, eliminating the need for a central server or platform. This disintermediation significantly reduces the transaction and administrative costs. The blockchain is particularly valuable in applications where reliability and stability are critical because it establishes an open database that ensures data integrity, making it virtually impossible to tamper with or falsify data. This study explores the diverse applications of the blockchain technology in virtual assets, such as cryptocurrency, decentralized finance, central bank digital currency, nonfungible tokens, and metaverses. In addition, it analyzes the potential prospects and developments driven by these innovative technologies.

• Structural Engineering and Mechanics
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• v.84 no.2
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• pp.143-154
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• 2022

Corrosion of steel reinforcement is considered as the main cause of concrete structures deterioration, especially those under humid environmental conditions. Hence, fiber reinforced polymer (FRP) bars are being increasingly used as a replacement for conventional steel owing to their non-corrodible characteristics. However, predicting the shear strength of beams reinforced with FRP bars still challenging due to the lack of robust shear theory. Thus, this paper aims to develop an explicit data driven based model to predict the shear strength of FRP reinforced beams using multi-objective evolutionary polynomial regression analysis (MOGA-EPR) as data driven models learn the behavior from the input data without the need to employee a theory that aid the derivation, and thus they have an enhanced accuracy. This study also evaluates the accuracy of predictive models of shear strength of FRP reinforced concrete beams employed by different design codes by calculating and comparing the values of the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R²), and percentage of prediction within error range of ±20% (a20-index). Experimental database has been developed and employed in the model learning, validation, and accuracy examination. The statistical analysis illustrated the robustness of the developed model with MAE, RMSE, 𝜇, 𝜎, R², and a20-index of 14.6, 20.8, 1.05, 0.27, 0.85, and 0.61, respectively for training data and 10.4, 14.1, 0.98, 0.25, 0.94, and 0.60, respectively for validation data. Furthermore, the developed model achieved much better predictions than the standard predictive models as it scored lower MAE, RMSE, and 𝜎, and higher R² and a20-index. The new model can be used in future with confidence in optimized designs as its accuracy is higher than standard predictive models.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.133-141
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• 2021

This research suggests a design process that effectively complements the human-centered design through an objective data-driven approach. The subjective human-centered design process can often lack objectivity and can be supplemented by the data-driven approaches to effectively discover hidden user needs. This research combines the data mining analysis with co-design process and verifies its applicability through the case study on the smart heating jacket. In the data mining process, the clustering can group the users which is the basis for selecting the target groups and the decision tree analysis primarily identifies the important user perception attributes and values. The broad point of view based on the data analysis is modified through the co-design process which is the deeper human-centered design process by using the developed workbook. In the co-design process, the journey maps, needs and pain points, ideas, values for the target user groups are identified and finalized. They can become the basis for starting new product development.

• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.2
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• pp.81-100
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• 2016

Much effort has been exerted to analyze online texts and understand how empirical results can help improve sales performance. In this research, we aim to extend this stream of research by decomposing online texts based on text sources, namely, companies and consumers. To be specific, we investigate how online texts driven by companies differ from those generated by consumers, and the extent to which both types of online texts have different effects on online sales. We obtained sales data from one of the biggest game publishers and merged them with online texts provided by companies using news articles and those created by consumers in user communities. The empirical analyses yield the following findings. Word visualization and topic analyses show that firms and consumers generate different contexts. Specifically, companies spread word to promote their own events whereas consumers produce online words to share winning strategies. Moreover, online sales are influenced by consumer-generated community topics whereas firm-driven topics in news articles have little to no effect. These findings suggest that companies should focus more on online texts generated by consumers rather than spreading their own words. Moreover, online sales strategies should take advantage of specific topics that have been proven to increase online sales. In particular, these findings give startup companies and small business owners in variety of industries the advantage when they use the online channel for distribution and as a marketing platform.

• Smart Structures and Systems
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• v.10 no.4_5
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• pp.353-373
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• 2012

The instrumented Canton Tower is a 610 m high-rise structure, which has been considered as a benchmark problem for structural health monitoring (SHM) research. In this paper, an improved automatic modal identification method is presented based on a natural excitation technique in conjunction with the eigensystem realization algorithm (NExT/ERA). In the proposed modal identification method, damping ratio, consistent mode indicator from observability matrices (CMI_O) and modal amplitude coherence (MAC) are used as criteria to distinguish the physically true modes from spurious modes. Enhanced frequency domain decomposition (EFDD), the data-driven stochastic subspace identification method (SSI-DATA) and the proposed method are respectively applied to extract the modal parameters of the Canton Tower under different environmental conditions. Results of modal parameter identification based on output-only measurements are presented and discussed. User-selected parameters used in those methods are suggested and discussed. Furthermore, the effect of environmental conditions on the dynamic characteristics of Canton tower is investigated.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.11
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• pp.87-96
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• 2021

Data-driven markets depend on access to data as a resource for products and services. Since the quality of information that can be drawn from data increases with the available amount and quality of the data, businesses involved in the data economy have a great interest in accessing data from other market players and sharing data with other stakeholders. Despite the growing need for access to data and evidence of the economic and social benefits, data access and sharing remains below its potential. Individuals, businesses, and governments often face barriers to data access, which may be compounded by the reluctance to share, including within and across sectors. To address these challenges, this paper focuses on finding possible solutions for a better data-sharing economy. This paper 1) Discusses opportunities and challenges of open data and the data-sharing economy, limitations of private sector data, and issues with open government data. 2) Introduces open government data initiatives and open governance networks initiatives. 3) Suggests possible solutions, including the governance and management, the legal and policy frameworks, and the technical standards for open data with proposing an open data governance model for the data-sharing economy.

• Journal of Distribution Science
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• v.12 no.12
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• pp.83-92
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• 2014

Purpose - A business ecosystem refers to mutually dependent systems interconnected by a loose foundation of various ecosystem members such as customers, suppliers, partners, and other stakeholders. The ecosystem-based strategy attempts to achieve competitive advantage for firms by enriching a business ecosystem or building a sustainable business ecosystem through the collaboration and co-evolution of its members. A sustainable business ecosystem is a source of competitiveness for firms anda manageable resource for gaining a competitive advantage. Customers represent the core membership of the business ecosystem and play a pivotal role in building a sustainable business ecosystem. This study examines the effects of customer participation on economic and social value in the business ecosystem and suggests a course of action for building a sustainable business ecosystem. Research design, data, and methodology - Two business cases of South Korea are selected from two different business types: business-to-business (B2B) and business-to-customer (B2C) firms. Business ecosystems for B2B and B2C firms reflect contrasting characteristics. Data was collected from in-depth interviews with four representatives of four firms. Results - The study suggested seven propositions for the relationships between customer participation and a sustainable business ecosystem through multiple case studies based on in-depth interviews. The results reveal the following four strategic actions for building sustainable business ecosystems based on the suggested propositions: alignment, systemization, socialization, and co-evolution. Alignment refers to achieving a harmonic balance or virtuous circle among the firm's mission, investment, and value creation. Systemization refers to building and implementing management and infrastructure systems rooted in the corporate culture. Socialization of customers in the business ecosystem reinforces the harmony or virtuous cycle. Finally, co-evolution is associated with the relationship between firms and customers as buyer firms in a restricted business ecosystem. Conclusions - This study considers multiple cases for the execution of a sustainable business ecosystem in collaboration with customers and suggests seven propositions and four strategic actions. The results are based on qualitative data from interviews with business associates from two firms in an open business ecosystem and two firms in a restricted business ecosystem, both in South Korea. Our research results regarding two contrasting business ecosystems shed light on business issues and policy making in Asian business environments, which are in the transition stages from a traditional conglomerate-driven to an inclusive growth-driven economy. The business ecosystem itself should be considered a manageable resource for firms' competitive positions in the market. A customer is a member of the business ecosystem and should thus be viewed not only as a purchasing entity and an object of relationship management but also as a co-creator of value. Therefore, firms should collaborate with customers to build sustainable business ecosystems. For this, firms must create social value, which cannot be created by customers alone, within the business ecosystem. Then, customers participate in a business ecosystem and build it to be favorable to them. Implications for academics and practitioners were suggested.