• The Journal of Information Systems
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• v.26 no.4
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• pp.227-246
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• 2017

Purpose: As the importance of software has been emphasized recently, the size of the software market is continuously expanding. The development of the software market is being adversely affected by software piracy. In this study, we try to classify countries around the world based on the macro environmental factors, which influence software piracy. We also try to identify the differences in software piracy for each classified type. Design/methodology/approach: The data-driven approach is used in this study. From the BSA, the World Bank, and the OECD, we collect data from 1990 to 2015 for 127 environmental variables of 225 countries. Cronbach's ${\alpha}$ analysis, item-to-total correlation analysis, and exploratory factor analysis derive 15 constructs from the data. We apply two-step approach to cluster analysis. The number of clusters is determined to be 5 by hierarchical cluster analysis at the first step, and the countries are classified by the K-means clustering at the second step. We conduct ANOVA and MANOVA in order to verify the differences of the environmental factors and software piracy among derived clusters. Findings: The five clusters are identified as underdeveloped countries, developing countries, developed countries, world powers, and developing country with large market. There are statistically significant differences in the environmental factors among the clusters. In addition, there are statistically significant differences in software piracy rate, pirated value, and legal software sales among the clusters.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.1
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• pp.255-283
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• 2024

In this study, as a preliminary research to effectively support data-driven R&D of researchers, we analyzed the academic information and data requirements for researchers to discover new types of academic information and datasets, and to propose directions for academic information services. To achieve the research objectives, we conducted an exploratory case study involving five researchers and administered an online survey among ScienceON users to glean insights into data-driven R&D behaviors and information/data requirements. As a result, researchers relatively referred to academic papers, datasets and software information from academic papers or conference materials. Moreover, the methods and pathways for acquiring data, as well as the types of data, varied across different subject areas. Researchers often faced challenges in data-driven R&D due to difficulties in locating and accessing necessary datasets or software such as learning models. Therefore it has been analyzed that for future support of data-driven R&D, there is a need to systematically construct datasets by subject. Additionally, it is considered necessary to extract and summarize dataset and related software information in conjunction with academic papers.

• Journal of Korea Water Resources Association
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• v.43 no.3
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• pp.257-264
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• 2010

The amount and continuity of precipitation data used in a hydrological analysis may exert a big influence on the reliability of the analysis. It is a fundamental process to estimate the missing data caused by such as a breakdown of the rainfall recording machine or to expand a short period of rainfall data. In this study a linear programming method treated as a data-driven approach for estimating the missing rainfall data is compared with seven other methods widely used and its superiority is certified. The data used in this research are annual precipitation ones during 17 years at the Cheolwon station including an ungauged period of 15 years and its five surrounding stations. By use of this certified method the ungauged precipitation values at the Cheolweon station are estimated and the areal averages of annual precipitation data for 32 years at the Han River basin are calculated.

• Smart Structures and Systems
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• v.20 no.2
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• pp.115-126
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• 2017

The modal identification of large civil structures such as bridges under the ambient vibrational conditions has been widely investigated during the past decade. Many operational modal analysis methods have been proposed and successfully used for identifying the dynamic characteristics of the constructed bridges in service. However, there is very limited research available on reliable criteria for the robustness of these identified modal parameters of the bridge structures. In this study, two time-domain operational modal analysis methods, the data-driven stochastic subspace identification (SSI-DATA) method and the covariance-driven stochastic subspace identification (SSI-COV) method, are employed to identify the modal parameters from field recorded ambient acceleration data. On the basis of the SSI-DATA method, the modal contribution indexes of all identified modes to the measured acceleration data are computed by using the Kalman filter, and their applicability to evaluate the robustness of identified modes is also investigated. Here, the benchmark problem, developed by Hong Kong Polytechnic University with field acceleration measurements under different excitation conditions of a cable-stayed bridge, is adopted to show the effectiveness of the proposed method. The results from the benchmark study show that the robustness of identified modes can be judged by using their modal contributions to the measured vibration data. A critical value of modal contribution index of 2% for a reliable identifiability of modal parameters is roughly suggested for the benchmark problem.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.317-320
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• 2015

Documenting work-in-progress on construction sites using images captured with smartphones, point-and-shoot cameras, and Unmanned Aerial Vehicles (UAVs) has gained significant popularity among practitioners. The spatial and temporal density of these large-scale site image collections and the availability of 4D Building Information Models (BIM) provide a unique opportunity to develop BIM-driven visual analytics that can quickly and easily detect and visualize construction progress deviations. Building on these emerging sources of information this paper presents a pipeline for model-driven visual analytics of construction progress. It particularly focuses on the following key steps: 1) capturing, transferring, and storing images; 2) BIM-driven analytics to identify performance deviations, and 3) visualizations that enable root-cause assessments on performance deviations. The information requirements, and the challenges and opportunities for improvements in data collection, plan preparations, progress deviation analysis particularly under limited visibility, and transforming identified deviations into performance metrics to enable root-cause assessments are discussed using several real world case studies.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.764-767
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• 2004

The performance of a speech recognition system is generally degraded in telephone environment because of distortions caused by background noise and various channel characteristics. In this paper, data-driven temporal filters are investigated to improve the performance of a specific recognition task such as telephone speech. Three different temporal filtering methods are presented with recognition results for Korean connected-digit telephone speech. Filter coefficients are derived from the cepstral domain feature vectors using the principal component analysis.

• KEPCO Journal on Electric Power and Energy
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• v.4 no.2
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• pp.101-105
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• 2018

In this paper, the kernel regression model is applied for the case study of gas turbine abnormal state analysis. In addition to vibration analysis at the remote site, the kernel regression model technique can is useful for analyzing abnormal state of rotor vibration signals of gas turbine in power plant. In monitoring based on data-driven techniques correlated measurements, the fault free training data of shaft vibration obtained during normal operations of gas turbine are used to develop a empirical model based on auto-associative kernel regression. This data-driven model can be used to predict virtual measurements, which are compared with real-time data, generating residuals. Any faults in the system may cause statistically abnormal changes in these residuals and could be detected. As the result, the kernel regression model provides information that can distinguish anomalies such as sensor failure in a shaft vibration signal.

• The Korean Journal of Air & Space Law and Policy
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• v.33 no.2
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• pp.345-366
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• 2018

Article 37 of the International Convention on Civil Aviation requires that rules should be adopted to keep in compliance with international standards and recommended practices established by ICAO. As SARPs are revised annually, each ICAO Member State needs to reflect the new content in its national aviation Acts in a timely manner. In recent years, data-driven international standards have been developed because of the important roles of aviation safety data and information-based legislation in accident prevention based on human factors. The Safety Management System and crew Fatigue Risk Management Systems were reviewed as examples of the result of data-driven rulemaking. The safety management system was adopted in 2013 with the introduction of Annex 19 and Chapter 5 of the relevant manual describes safety data collection and analysis systems. Through analysis of safety data and information, decision makers can make informed data-driven decisions. The Republic of Korea introduced Safety Management System in accordance with Article 58 of the Aviation Safety Act for all airlines, maintenance companies, and airport corporations. To support the SMS, both mandatory reporting and voluntary safety reporting systems need to be in place. Up until now, the standard of administrative penal dispensation for violations of the safety management system has been very weak. Various regulations have been developed and implemented in the United States and Europe for the proper legislation of the safety management system. In the wake of the crash of the Colgan aircraft, the US Aviation Safety Committee recommended the US Federal Aviation Administration to establish a system that can identify and manage pilot fatigue hazards. In 2010, a notice of proposed rulemaking was issued by the Federal Aviation Administration and in 2011, the final rule was passed. The legislation was applied to help differentiate risk based on flight according to factors such as the pilot's duty starting time, the availability of the auxiliary crew, and the class of the rest facility. Numerous amounts data and information were analyzed during the rulemaking process, and reflected in the resultant regulations. A cost-benefit analysis, based on the data of the previous 10 year period, was conducted before the final legislation was reached and it was concluded that the cost benefits are positive. The Republic of Korea also currently has a clause on aviation safety legislation related to crew fatigue risk, where an airline can choose either to conform to the traditional flight time limitation standard or fatigue risk management system. In the United States, specifically for the purpose of data-driven rulemaking, the Airline Rulemaking Committee was formed, and operates in this capacity. Considering the advantageous results of the ARC in the US, and the D4S in Europe, this is a system that should definitely be introduced in Korea as well. A cost-benefit analysis is necessary, and can serve to strengthen the resulting legislation. In order to improve the effectiveness of data-based legislation, it is necessary to have reinforcement of experts and through them prepare a more detailed checklist of relevant variables.

• Journal of Korea Society of Industrial Information Systems
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• v.2 no.2
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• pp.189-207
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• 1997

Recently, we have witnessed a host of emerging tools in the management support systems (MSS) area including the data warehouse/multidimensinal databases (MDDB), data mining, on-line analytical processing (OLAP), intelligent agents, World Wide Web(WWW) technologies, the Internet, and corporate intranets. These tools are reshaping MSS developments in organizations. This article reviews a set of emerging data management technologies in the knowledge discovery in databases(KDD) process and analyzes their implications for decision support. Furthermore, today's MSS are equipped with a plethora of AI techniques (artifical neural networks, and genetic algorithms, etc) fuzzy sets, modeling by example , geographical information system(GIS), logic modeling, and visual interactive modeling (VIM) , All these developments suggest that we are shifting the corporate decision making paradigm form information-driven decision making in the1980s to knowledge-driven decision making in the 1990s.

• East Asian Journal of Business Economics (EAJBE)
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• v.12 no.1
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• pp.13-22
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• 2024

Purpose: The purpose of this study is to investigate impact of smart city awareness on citizen satisfaction and to measure various factors influencing smart city competitiveness that were rarely addressed in previous studies. For the impacts on the competitiveness of smart cities, this study explored the effects of data-driven service, economic impact, social trust through sharing, environmental protection, and sustainable growth. Research design, data and methodology: To collect data, this study employed an online survey conducted by a reputable research organization. Data analysis involved the use of factor analysis, ANOVA, and regression analysis. Results: This study identified key aspects important for enhancing citizen satisfaction. Furthermore, this research unveiled the significant impacts of data-driven service, economic impact, social trust through sharing, environmental protection, and sustainable growth on the competitiveness of smart cities. Conclusions: The results yield valuable managerial and policy implications. The study suggests that enhancing citizen satisfaction through improved awareness of the smart city is crucial for effective city marketing management. Additionally, the results highlight special aspects necessary to improve smart city competitiveness, including the implementation of promotional policies supported by the government, promoting global competitiveness for domestic companies, and fostering citizen participation for effective city marketing management.