• Journal of the Korean Data and Information Science Society
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• v.16 no.3
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• pp.567-580
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• 2005

It is usually assumed that asset returns in the stock market are normally distributed. However, analyses of real data show that the distribution tends to be skewed and to have heavier tails than those of the normal distribution. In this paper, we investigate the method of estimating the value at risk(VaR) of stock returns. The VaR is computed by using the transformation and back-transformation method. The analysis of KOSPI and KOSDAQ data shows that the proposed estimation outperformed that under the normal assumption.

• Journal of the Korean Data and Information Science Society
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• v.12 no.1
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• pp.103-125
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• 2001

The Box-Cox transformation is a well known family of power transformation that brings a set of data into agreement with the normality assumption of the residuals and hence the response variable of a postulated model in regression analysis. This paper proposes a new method for estimating the Box-Cox transformation using maximization of the Sample Entropy statistic which forces the data to get closer to normal as much as possible. A comparative study of the proposed procedure with the maximum likelihood procedure, the procedure via artificial regression estimation, and the recently introduced maximization of the Shapiro-Francia W' statistic procedure is given. In addition, we generate a table for the optimal spacings parameter in computing the Sample Entropy statistic.

• Journal of Integrative Natural Science
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• v.13 no.2
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• pp.76-82
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• 2020

If normality of an observed data is not a viable assumption, we can carry out normal-theory analyses by suitable transforming data. Power transformation by Box and Cox, one of the transformation methods, is derived the power which maximized the likelihood function. But it doesn't induces the closed form in mathematical analysis. In this paper, we compose some R the syntax of which is easier than other statistical packages for deriving the power with using numerical methods. Also, by using R, we show the transformed data approximately distributed the normal through Q-Q plot in univariate and bivariate cases with some examples. Finally, we present the value of a goodness-of-fit statistic(AD) and its p-value for normal distribution. In the similar procedure, this method can be extended to more than bivariate case.

• International Journal of Knowledge Content Development & Technology
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• v.14 no.3
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• pp.77-91
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• 2024

This article aims to comprehensively analyze the crucial role played by data analytics and business intelligence (BI) strategies in propelling digital transformation within diverse industries. Through an extensive literature review and examination of real-world case studies, the study employs a systematic analysis of scholarly works and industry reports. This approach provides a panoramic view of how organizations utilize data-driven insights for competitive advantages, improved customer experiences, and fostering innovation. The findings underscore the pivotal significance of data analytics and BI strategies in influencing strategic decision-making, enhancing operational efficiency, and ensuring long-term sustainability across various industries. The study stands out in its originality by offering a unique synthesis of insights derived from scholarly works and real-world case studies, contributing to a holistic understanding of the transformative impact of data analytics and BI on contemporary business practices. While the study provides valuable insights, limitations include the scope of available literature and case studies. The implications call for further research to explore emerging trends and evolving challenges in the dynamic landscape of data analytics and BI. The practical implications highlight the tangible benefits organizations can derive from integrating data analytics and BI strategies, emphasizing their role in shaping strategic decisions and fostering operational efficiency. In a broader context, the study delves into the social implications of the symbiotic relationship between data analytics, BI, and digital transformation. It explores how these strategies impact broader societal and economic aspects, influencing innovation and sustainability.

• Information Systems Review
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• v.6 no.2
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• pp.65-77
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• 2004

Business intelligence assumes an integrated and inter-connected information resources. To manage an integrated database, we need to trace data transformation processes from its outset. For this purpose, this study proposes an extended Information Structure Graph that models data transformation steps in addition to data transformation structures. Using the graph, we can identify relationship among data entities and assign data quality measures to each nodes or arcs of a graph, thus eases management of data and enhancing their quality.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.2
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• pp.141-148
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• 2007

With the use of world geodetic system, the UIS DB of WonJu City, which was established on Korea coordinate system becomes to face the need of coordinates transformation. However, the UIS DB of Wonju City is based on dualistic control points of Korea geodetic system, which means the coordinates system is not standardized. Therefore it is required to establish a plan for transformation into world geodetic system. For this purpose, we calculated coordinate transformation coefficients per control point data of Korea geodetic system and verified the accuracy of transformation coefficients. Through the process, we propose a plan for transforming the coordinate system of UIS DB in WonJu City, which has been based on dualistic control point data of Korea geodetic system, efficiently into the world geodetic system.

• Journal of Distribution Science
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• v.22 no.5
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• pp.39-57
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• 2024

Purpose: Various policies continue to be strengthened to develop Micro, Small and Medium Enterprises (MSMEs), which have a strategic role in the economy through the pillars of corporatization, capacity and financing to support strong and inclusive economic growth. Efforts to transform MSMEs marketing strategies are undertaken through eco-friendly digitalization to increase resilience and more productive and innovative capacity. Research design, data and methodology: This research is an exploratory qualitative approach taken to investigate the transformation of eco-friendly marketing strategies for MSMEs to increase competitiveness at the global level. The samples obtained were 425 MSMEs assisted by the DKI Jakarta, Bali, Java, Borneo, and Sumatera. The data collection technique used non-probability sampling (snowball sampling). Data is analyzed through collection, reduction, analysis, validity testing, presentation and conclusion. Results: This research shows the transformation of eco-friendly digital-based MSME marketing strategies occurred through four stages, namely production and institutional activities, expanding market share, digitalization and financing, and export market access. Conclusions: Eco-friendly digital transformation allows MSMEs competencies to be refined to improve business processes and business competitiveness at the international level. The contribution of this marketing strategy transformation is expanding MSMEs access to financial institutions (fintech), marketplaces, and QRIS (QR Code Indonesian Standard) digital payments.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.8
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• pp.235-242
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• 2020

In order to promote digital transformation of our agricultural and rural communities in the wake of the fourth industrial revolution and prepare for the upcoming artificial intelligence era, it is necessary to establish a system and system that can collect, analyze and utilize necessary quality data. To this end, we will investigate and analyze problems and issues felt by various stakeholders such as farmers and agricultural officials, and present strategic measures to revitalize big data, which must be decided in order to promote digital transformation of our agricultural and rural communities, such as expanding big data platforms for joint utilization, establishing sustainable big data governance, and revitalizing the foundation for big data utilization based on demand.

• Journal of Sensor Science and Technology
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• v.28 no.3
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• pp.182-186
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• 2019

When a camera is employed for 3D sensing, accurate camera calibration is vital as it is a prerequisite for the subsequent steps of the sensing process. Camera calibration is usually performed by complex mathematical modeling and geometric analysis. On the other contrary, data learning using an artificial neural network can establish a transformation relation between the 3D space and the 2D camera image without explicit camera modeling. However, a neural network requires a large amount of accurate data for its learning. A significantly large amount of time and work using a precise system setup is needed to collect extensive data accurately in practice. In this study, we propose a two-step neural calibration method that is effective when only a small amount of learning data is available. In the first step, the camera projection transformation matrix is determined using the limited available data. In the second step, the transformation matrix is used for generating a large amount of synthetic data, and the neural network is trained using the generated data. Results of simulation study have shown that the proposed method as valid and effective.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.67
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• pp.49-60
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• 2001

Data warehouse(DW) manages all information in a Enterprise and also offers the specific information to users. However, it might be difficult to develope an effective DW system due to varieties in computing facilities, data base, and operating systems. The heterogeneous system environments make it harder to extract data and to provide proper information to usesr in real time. Also commonly occurred is data inconsistency of non-integrated legacy system, which requires an effective and efficient data extraction flow control as well as data cleansing. We design the integrated automatic ETT(Extraction Transformation Transportation) system to control data extraction flow and suggest implementation methodology. Detail analysis and design are given to specify the proposed ETT approach with a real implementation.