• Human Ecology Research
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• v.59 no.2
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• pp.245-259
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• 2021

The purpose of this study was to analyze big data to identify the sub-dimensions of ethical consumption, as well as the consumption value associated with ethical consumption that changes over time. For this study, data were collected from Naver and Daum using the keyword 'ethical consumption' and frequency and matrix data were extracted through Textom, for the period January 1, 2016, to December 31, 2018. In addition, a two-way mode network analysis was conducted using the UCINET 6.0 program and visualized using the NetDraw function. The results of text mining show increasing keyword frequency year-on-year, indicating that interest in ethical consumption has grown. The sub-dimensions derived for 2014 and 2015 are fair trade, ethical consumption, eco-friendly products, and cooperatives and for 2016 are fair trade, ethical consumption, eco-friendly products and animal welfare. The results of deriving consumption value keywords were classified as emotional value, social value, functional value and conditional value. The influence of functional value was found to be growing over time. Through network analysis, the relationship between the sub-dimensions of ethical consumption and consumption values derived each year from 2014 to 2018 showed a significantly strong correlation between eco-friendly product consumption and emotional value, social value, functional value and conditional value.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10a
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• pp.21-23
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• 1998

본 논문에서 ILP (Instruction Level Parallelism)의 성능향상을 위하여 데이터 값들을 미리 예측하여 병렬로 이슈(issue)하고 수행하는 기존의 데이터 값 예측기(data value predictor)를 비교 분석하여 각 예측기의 예측율을 측정하고, 2-단계 데이터 값 예측기(Two-Level Data Value Predictor)와 혼합형 데이터 값 예측기(Hydrid Data Value Predictor)에서 발생되는 aiasing 을 측정하기 위해 수정된 데이터 값 예측기를 사용하여 측정한 결과 aliasing은 50% 감소하였지만 예측율에는 영향을 미치지 못함과 데이터 값 예측기의 예측율을 측정한 결과 혼합형 데이터 값 예측기의 예측율이 2-단계 데이터 값 예측기와 스트라이드 데이터 값 예측기(Stride Data Value Predictor)에서 평균 5.7%, 최근 값 예측기(Last Data Value Predictor)보다는 평균 38%의 예측 정확도가 높음을 입증하였다.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.34-46
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• 2021

In the domestic data exchanging market, unreasonable pricing of purchase data is consistently mentioned as a major obstacle in data trading. This is a problem caused by the inability to properly evaluate the value of data products due to lack of product information and experience in using them. In order to activate trading, the data exchanges need to provide information that allows consumers to comprehensively judge the value of data products in addition to prices. The cost-based, income-based, and market-based methods, which are mainly applied to data valuation, are insufficient as data valuation methods to stimulate trading and distribution because only price information, a result of valuation from a supplier's point of view, can be shared with consumers. This study aims to develop a measurable valuation method that allows data trading stakeholders (exchanges, suppliers, and consumers) to judge and share the value of data products from a common perspective. To this end, we identified the value drivers of data products, which are considered important in overseas data exchanges and related research, and derived an evaluation method that can quantitatively measure each value driver. In addition, evaluation criteria in the form of a rating table were developed using data products for transactions, and a value evaluation index was developed through stratification analysis (AHP) to enable relative value comparison. As a result of applying the evaluation criteria to actual data products, it was found that the evaluation values were differentiated according to the characteristics of individual data products, so it could be used as a relative value comparison tool.

• Journal of the Korean Data and Information Science Society
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• v.9 no.1
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• pp.63-70
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• 1998

The singular value decomposition is a tool which is used to find a linear structure of reduced dimension and to give interpretation of the lower dimensional structure about multivariate data. In this paper the singular value decomposition is reviewed from both algebraic and geometric point of view and, is illustrated the way which the tool is used in the multivariate techniques finding a simpler geometric structure for the data.

• Journal of Information Technology Services
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• v.18 no.1
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• pp.113-130
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• 2019

Data can be considered a core driver of the Fourth Industrial Revolution, and databases are needed to create value by efficiently obtaining, storing and analyzing the data. However, there are currently no adequate valuation methods to handle databases. This study aims first, to understand databases as the subject of valuation and analyze value drivers of databases, and second, to propose a database valuation model based on this finding. To this end, we derive value drivers of databases from the characteristics and value criteria of databases observed in previous studies. Based on survey data from 396 database service firms we verify the value drivers through linear regression analysis. We find that the annual growth rate in database capacity and the data types positively affect sales of databases and offer ways to utilize them when estimating the cash flow, which is the variable to apply the discounted cash flow method-based income approach. This study contributes empirical insights into how to valuate databases considering their value drivers.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.338-345
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• 2023

Nowadays, many companies are striving to turn customer value into business value. Customer Relationship Management is a management system that develops effective and efficient marketing strategies by classifying customers in detail based on their information, i.e. databases, and consists of various information technologies. To implement this management system, a customer integration database must be established, and customer characteristics (buying behavior, preferences, etc.) must be analyzed with the databases established and the behavior of each customer must be predicted. This study aims to systematically manage a large amount of customer data generated by companies that apply Customer Relationship Management, in order to develop data design and data governance strategies that should be considered to increase customer value and even company value. We mainly looked at the characteristics of customer relationship management and data governance, and then explored the link between the field of customer relationship management and data governance. In addition, we have developed a data strategy that companies need to perform data governance for customer relationship management.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1387-1390
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• 2005

Recently, foreign utilities emphasize the importance of the value-added services based on Information Technology(IT) as one of the strategic technologies for establishing a new power system in the future digital society. They develop many different types of the value-added services and apply the systems for customer. In domestic case, the data from the Automatic Meter Reading (AMR) System is used only for calculating the tariffs. Data from the AMR system can be strategic assets for utilities to provide the value-added services for customer. Development of the value-added services for utilities and customer needs processing and managing the AMR data. In this paper, the energy consulting service, which is adequate to new power system environment, is suggested for development of the value-added services. The application of the suggested service will bring the effect of reducing the monthly bill for customer. Also the service will give not only the effective demand side management(DSM) and load control, but also reduction of the investment for utilities.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.15-26
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• 2014

This study focus on a economic value of the Big Data technologies by real options model using big data technology company's stock price to determine the price of the economic value of incremental assessed value. For estimating stochastic process of company's stock price by big data technology to extract the incremental shares, Generalized Moments Method (GMM) are used. Option value for Black-Scholes partial differential equation was derived, in which finite difference numerical methods to obtain the Big Data technology was introduced to estimate the economic value. As a result, a option value of big data technology investment is 38.5 billion under assumption which investment cost is 50 million won and time value is a about 1 million, respectively. Thus, introduction of big data technology to create a substantial effect on corporate profits, is valuable and there are an effects on the additional time value. Sensitivity analysis of lower underlying asset value appear decreased options value and the lower investment cost showed increased options value. A volatility are not sensitive on the option value due to the big data technological characteristics which are low stock volatility and introduction periods.

• Journal of the Korean Solar Energy Society
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• v.29 no.6
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• pp.102-109
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• 2009

The research on meteorological data in Korea has been carried out but without much consistency and has been limited to some areas only. Of relatively more importance has been the area in the utilization of the solar energy, however, the measurement of the global solar horizontal irradiation has been quite limited. In the current study, the actually measured value of the global solar horizontal irradiation from the meteorological data and the theoretically calculated value of the global solar horizontal irradiation from the cloud amount will be analyzed comparatively. The method of analysis will employ the standard meteorological data drafted by the Korean Solar Energy Society, the standard meteorological data from the presently used simulation program and the corresponding results have been compared with the calculated value of the global solar horizontal irradiation from the cloud amount. The results of comparing the values obtained from MBE(Mean Bias Error), RMSE(Root Mean Squares for Error), t-Statistic methods and those from each of the standard meteorological data show that the actually measured value of the meteorological data which have been converted into standard meteorological data with the help of the ISO TRY method give the monthly average value of the global solar horizontal irradiation. These values compared with the monthly average value from the IWEC from the Department of Energy of the USA show that the value of the global solar horizontal irradiation in the USA is quite similar. In the case of the values obtained from calculation from the cloud amount, the weather data provided by TRNSYS, except only slight difference, which means that the actually measured values of the global solar horizontal irradiation are significant. This goes to show that in the case of Korea, the value of the global solar horizontal irradiation provided by the Korea Meteorological Administration is will be deemed correct.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2002.09a
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• pp.28-34
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• 2002

The b-value in the magnitude-frequency relationship logN(m) = $\alpha$ - bmwhere N(m) is the number of earthquakes exceeding magnitude m, is important seismicity parameter In hazard analysis. Estimation of the b-value for earthquake data observed on KSRS array network is done employing the maximum likelihood technique. Assuming the whole Korea Peninsula as a single seismic source area, the b-value is computed at 0.9. The estimation for KMA earthquake data is also similar to that. Since estimate is a function of minimum magnitude, we can inspect the completeness of earthquake catalog in the fitting process of b-value. KSRS and KMA data lists are probably incomplete for magnitudes less than 2.0 and 3.0, respectively. Examples from probabilistic seismic hazard assessment calculated for a range of b-value show that the small change of b-value has seriously effect on the prediction of ground motion.