• Journal of Information Science Theory and Practice
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• 제9권3호
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• pp.14-30
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• 2021

Data innovation is at the core of the Fourth Industrial Revolution. While the catastrophic COVID-19 pandemic has accelerated the societal shift toward a data-driven society, the direction of overall data regulation remains unclear and data policy experts have yet to reach a consensus. This study identifies and examines the ideal regulator models of data-policy experts and suggests an appropriate method for developing policy in the data economy. To identify different typologies of data regulation, this study used Q methodology with 42 data policy experts, including public officers, researchers, entrepreneurs, and professors, and additional focus group interviews (FGIs) with six data policy experts. Using a Q survey, this study discerns four types of data policy regulators: proactive activists, neutral conservatives, pro-protection idealists, and pro-protection pragmatists. Based on the results of the analysis and FGIs, this study suggests three practical policy implications for framing a nation's data policy. It also discusses possibilities for exploring diverse methods of data industry regulation, underscoring the value of identifying regulatory issues in the data industry from a social science perspective.

• Journal of Information Technology Applications and Management
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• 제31권1호
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• pp.79-95
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• 2024

In modern society, as data plays a crucial role at the levels of businesses, industries, and nations, the utilization of data becomes increasingly important. Consequently, governments are prioritizing the development and implementation of plans to cultivate data workforce, viewing the data industry as a cornerstone of national strategy. To enhance domestic capabilities and nurture workforce in the data industry, it is deemed necessary to conduct an objective comparative analysis with major foreign countries. Therefore, this study aims to analyze cases of domestic and international data industries and explore methods for quantitatively comparing data industry workforce across nations. Initially, the study distinguishes between "data industry workforce" and "data job-related workforce," particularly focusing on professionals handling data-related tasks. Subsequently, it compares the workforce sizes of data job-related workforce across nations, utilizing standardized occupational classification codes based on the International Standard Classification of Occupations(ISCO). However, it should be noted that countries employing their own unique occupational classification systems often require matching job titles with similar meanings for accurate comparison. Through this study, it is anticipated that policymakers will be able to establish future directions for cultivating data workforce based on comparable status.

• 응용통계연구
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• 제23권1호
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• pp.197-206
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• 2010

본 논문에서는 대용량 자료 혹은 시간에 따라 순차적으로 들어오는 자료의 분류를 위한 부스팅(boosting) 알고리즘을 제안한다. 대용량 자료나 순차적 자료의 경우 분석시 모든 훈련 자료(training data)들을 한번에 이용하기 어려우므로 보통의 부스팅 알고리즘은 적절하지 못하다. 이러한 상황을 극복하기 위해 AdaBoost와 Arc-x4와 같은 부스팅 알고리즘을 수정하여 제안한다. 모의 실험과 실제 자료 분석을 통해 대용량 자료나 순차적 자료에 제안된 알고리즘이 잘 적용됨을 보였다.

• Journal of information and communication convergence engineering
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• 제14권1호
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• pp.9-13
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• 2016

With the introduction of the data-oriented plan for LTE service, one may concerned with the background of the ISP's policy in charging for LTE services. In this work we investigate the latest usage patterns of voice and data applications for customers over the current mobile network, via which we investigate why mobile operators introduced data-oriented plans. To be specific, we collected the real-field data for the volume of voice and data traffic from the LTE network before the data-oriented plans were introduced. From the collected data we compute the absolute volume as well as the proportion of voice and data applications. From these observations we infer mobile operators' reasoning behind the decision to introduce data-oriented plans with unlimited voice calls over the mobile network.

• Journal of the Korean Data and Information Science Society
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• 제16권3호
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• pp.515-527
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• 2005

The data mining is a new approach to extract useful information through effective analysis of huge data in numerous fields. We utilized this data mining technique to analyze medical record of 39,900 people. Whole data were separated by gender first and divided into three groups, including normal, stage 1 hypertension, and stage 2 hypertension. The data from each group were analyzed with data mining technique. Based on the result that we have extracted with this data mining technique, major risk factors for the hypertension are age, BMI score, family history.

• Journal of Advanced Marine Engineering and Technology
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• 제26권4호
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• pp.493-500
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• 2002

The operational data of diesel generator engine is two kinds of data. One is interactive the other is non interactive. We can find the fault information from interactive data measured for every sampling time when the changing rate, direction and status of data are investigated in comparition with those of normal status to diagnose the fault of combustion system. The various data values of combustion system for diesel engine are not proportional to load condition. The criterion to decide the level of data value is not absolute but relative to relational data. This study proposes to compose malfunction diagnosis engine using neural networks to decide that level of data value is out of normal status with the data collected from generator engine of the ship using the commercial data mining tool. This paper investigates the real ship's operational data of diesel generator engine and confirms usefulness of fault detecting through simulations for fault detecting.

• Journal of the Korean Data and Information Science Society
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• 제17권4호
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• pp.1181-1190
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• 2006

Statistical analyses for spatial data are important features for various types of fields. Spatial data are taken at specific locations or within specific regions and their relative positions are recorded. Lattice data are synoptic observation covering an entire spatial region, like cancer rates corresponding to each county in a state. Until now, the echelon analysis has been applied only to univariate spatial data. As a result, it is impossible to detect the hotspots on the multivariate spatial data In this paper, we expand the spatial data to time series structure. And then we analyze them on the time space and detect the hotspots. Echelon dendrogram has been made by piling up each multivariate spatial data to bring time spatial data. We perform the structural analysis of temporal spatial data.

• 한국컴퓨터정보학회논문지
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• 제23권3호
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• pp.49-54
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• 2018

A data warehouse is a system that collectively manages and integrates data of a company. And provides the basis for decision making for management strategy. Nowadays, analysis data volumes are reaching critical size challenging traditional data ware housing approaches. Current implemented solutions are mainly based on relational database that are no longer adapted to these data volume. NoSQL solutions allow us to consider new approaches for data warehousing, especially from the multidimensional data management point of view. In this paper, we extend the data warehouse design methodology based on relational database using star schema, and have developed a consistent design methodology from information requirement analysis to data warehouse construction for large scale data warehouse construction based on MongoDB, one of NoSQL.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제10권1호
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• pp.21-29
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• 2006

점 데이터로부터 곡선이나 곡면을 형성하는 방법은 널리 알려져 있다. 하지만 미분데이터가 주어지는 경우는 공학적으로 많지 않기 때문에 미분 데이터로부터 곡면을 형성하는 방법은 잘 사용되고 있지 않다. 여기서는 점 데이터, 1차 미분 데이터, 2차 미분 데이터 등 세 개중에 한 개 이상이 주어지면 곡면을 형성 할 수 있다. 1차 미분 데이터만 주어진 경우는 한 점에서 함수 값을 더한다. 2차 미분 데이터가 주어진 경우는 1차 함수를 추가하면 된다.

• Journal of the Korean Data and Information Science Society
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• 제16권4호
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• pp.879-888
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• 2005

Fashion is hard to expect owing to the rapid change in accordance with consumer taste and environment, and has a tendency toward variety and individuality. Especially street fashion of 21st century is not being regarded as one of the subcultures but is playing an important role as a fountainhead of fashion trend. Therefore, Searching and analyzing street fashions helps us to understand the popular fashions of the next season and also it is important in understanding the consumer fashion sense and commercial area. So, we need to understand fashion styles quantitatively and qualitatively by providing visual data and dividing images. There are many kinds of data in street fashion information. The purpose of this study is to design and implementation for street fashion information analysis system using data fusion. We can show visual information of customer's viewpoint because the system can analyze the fused data for image data and survey data.