• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.1-8
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• 2020

The use of text data in big data analytics has been increased. So, much research on methods for text data analysis has been performed. In this paper, we study Bayesian learning based on conjugate prior for analyzing keyword data extracted from text big data. Bayesian statistics provides learning process for updating parameters when new data is added to existing data. This is an efficient process in big data environment, because a large amount of data is created and added over time in big data platform. In order to show the performance and applicability of proposed method, we carry out a case study by analyzing the keyword data from real patent document data.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.539-544
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• 2006

This study has been attempted to find that factors for successful six-sigma implementation influence non-financial performance & financial performance in korean finance industry. In addition, goal of this study is to find out core factor in korean finance industry. To achieve the aim of this study, a document study and interview and an empirical analysis were performed. The collected questionnaires for the empirical analysis were processed statistically through data cording. Cronbach'a was conducted to get the construct reliability. To identify which factors for successful six-sigma implementation influence performances of six-sigma implementation, factor analysis was conducted to get the construct validity. After factor analysis, multiple regressions were utilized to identify the core factors (or factors for successful six-sigma implementation). The result of the study that has been derived through this process is summarized below. Firstly, by analyzing the effect factors for successful six-sigma implementation has on non-financial performance of finance industry, it shows that Process-integration & standardization variable has influenced. Secondly, by analyzing the effect factors for successful six-sigma implementation has on financial performance of finance industry, it shows that 'Process-integration & standardization' variables and 'Customer & Market mind' variables have influenced. The results of this study show that 'Process-integration & standardization' and 'Customer & Market mind' are core factors to influence non-financial performance & financial performance in korean finance industry

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1990.04a
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• pp.71-80
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• 1990

The main objectives of this paper are as follows: 1) identifying th procedural characteristics of application software package acquisition and, 2) analyzing the relationship between those characteristics and MIS performance. Three stages and thirty core tasks of the application software package acquisition process were identified after reviewing relevant literature. The model specifying the relationship between procedural characteristics of application software package acquisition and MIS performance was established and 12 hypotheses were derived. Data were collected form 41 Korean companies and hypotheses were tested empirically.

• The Korean Journal of Applied Statistics
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• v.37 no.4
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• pp.467-484
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• 2024

Longitudinal data are measured repeatedly over time from the same subject, so there is a correlation from the repeated outcomes. Therefore, when analyzing this correlation, both serial correlation and between-subject variation must be considered in longitudinal data analysis. In this paper, we will focus on the marginalized models to estimate the population average effect of covariates among models for analyzing longitudinal binary data. Marginalized models for longitudinal binary data include marginalized random effects models, marginalized transition models, and marginalized transition random effect models, and in this paper, these models are first reviewed, and simulations are conducted using complete data and missing data to compare the performance of the models. When there were missing values in the data, there is a difference in performance depending on the model in which the data was generated. We analyze Korea Health Panel data using marginalized models. The Korean Medical Panel data considers subjective unhealthy responses as response variables as binary variables, compares models with several explanatory variables, and presents the most suitable model.

• The Journal of the Korea Contents Association
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• v.13 no.2
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• pp.211-219
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• 2013

The analysis method using Big Data has evolved based on the Big data Management Technology. There are quite a few researching institutions anticipating new era in data analysis using Big Data and IT vendors has been sided with them launching standardized technologies for Big Data management technologies. Big Data is also affected by improvements of IT gadgets IT environment. Foreran by social media, analyzing method of unstructured data is being developed focusing on diversity of analyzing method, anticipation and optimization. In the past, data analyzing methods were confined to the optimization of structured data through data mining, OLAP, statics analysis. This data analysis was solely used for decision making for Chief Officers. In the new era of data analysis, however, are evolutions in various aspects of technologies; the diversity in analyzing method using new paradigm and the new data analysis experts and so forth. In addition, new patterns of data analysis will be found with the development of high performance computing environment and Big Data management techniques. Accordingly, this paper is dedicated to define the possible analyzing method of social media using Big Data. this paper is proposed practical use analysis for social media analysis through data mining analysis methodology.

• Journal of Information Technology Services
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• v.17 no.3
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• pp.171-189
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• 2018

To remove software defects and improve performance of software, many developers perform code inspections and use static analysis tools. A code inspection is an activity that is performed manually to detect software defects in the developed source. However, there is no clear criterion which source codes are inspected. A static analysis tool can automatically detect software defects by analyzing the source codes without running the source codes. However, it has disadvantage that analyzes only the codes in the functions without analyzing the relations among source functions. The functions in the source codes are interconnected and formed a social network. Functions that occupy critical locations in a network can be important enough to affect the overall quality. Whereas, a static analysis tool merely suggests which functions were called several times. In this study, the core functions will be elicited by using social network analysis and DEA (Data Envelopment Analysis) for CUBRID open database sources. In addition, we will suggest clear criteria for selecting the target sources for code inspection and will suggest ways to find core functions to minimize defects and improve performance.

• 한국디지털정책학회:학술대회논문집
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• 2004.05a
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• pp.575-592
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• 2004

In modern dynamic management environment, there is growing recognition that? information & knowledge management systems are essential for CEO's efficient/effective decision making. As a key component to cope with this current, we suggest the management performance analysis syystem based on Knowledge Discovery in Database (KDD). The system measures management performance that is considered with both VA(Value- Added), which represents stakeholder's point of view and EVA(Economic Value-Added), which represents shareholder's point of view. The relation ship between management performance and some 80 financial ratios is analyzed, and then important financial ratios are drawn out. In analyzing the relationship, we applied KDD process which includes such as multidimensional cube, OLAP(On-Line Analytic Process), data mining and AHP(Analytic Hierarchy Process). To demonstrate the performance of the system, we conducted a case study using financial data over the 16-years from 1981 to 1996 of Korean automobile industry which is taken from database of KISF AS(Korea Investors Services Financial Analysis System).

• 한국디지털정책학회:학술대회논문집
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• 2003.12a
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• pp.191-210
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• 2003

In modern dynamic management environment, there is growing recognition that information & knowledge management systems are essential for CEO's Efficient/effective decision making, As a key component to cope with this current, we suggest the management performance analysis system based on Knowledge Discovery in Database (KDD). The system measures management performance that is considered with both VA(Value-Added), which represents stakeholder's point of view and EVA(Economic Value-Added), which represents shareholder's point of view, The relationship between management performance and some 80 financial ratios is analyzed, and then important financial ratios are drawn out. In analyzing the relationship, we applied KDD process which includes such as multidimensional cube, OLAP(On -Line Analytic Process), data mining and AHP(Analytic Hierarchy Process). To demonstrate the performance of the system, we conducted a case study using financial data over the 16-years from 1981 to 1996 of Korean automobile industry which is taken from database of KISFAS(Korea Investors Services Financial Analysis System).

• Korean Journal of Applied Biomechanics
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• v.13 no.3
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• pp.341-354
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• 2003

The purpose of this study was to develop the uniaxial force plate system which is measured by the vertical force. The VGRF(vertical ground reaction force) 1.0 was composed of 2 bath digital scales, 2 indicaters, and analyzing software. This system was newly renovated to VGRF 2,0 which are 2 industrial digital scales, 2 adjustable indicators, and enforced analyzing software. Changes of the new system were as follows. First, the height of the plate was 75% lower than before. Second, sensing ability of the load cell was changed from 90 - 0.05kg to 300 - 0.1kg. Third, the speed of data processing was changed from 17 per second to 60 per second. Fourth, analyzing software was enforced to develop and calculate the data. For the test of the system, two different types(bare foot, high-heeled shoes) gait was adopted. highly skilled female walker(23yrs, height 165cm, body mass 46.8kg) participated for the experimental study. During the dynamic performance(gait analysis), the data of each load cell were very similar to the previous studies. Specifically, bare foot walking had less vertical force than high-heeled shoes. Consequently, VGRF 2.0 can sense the general dynamic movements as well as static load conditions.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.31 no.3
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• pp.42-49
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• 2023

Not only in aviation industry but also in other industries, safety data plays a key role to improve the level of safety performance. By analyzing safety data such as aviation safety report (text data), hazard can be identified and removed before it leads to a tragic accident. However, pre-processing of raw data (or natural language data) collected from each site should be carried out first to utilize proactive or predictive safety management system. As air traffic volume increases, the amount of data accumulated is also on the rise. Accordingly, there are clear limitation in analyzing data directly by manpower. In this paper, a topic prediction model for aviation safety mandatory report is proposed. In addition, the prediction accuracy of the proposed model was also verified using actual aviation safety mandatory report data. This research model is meaningful in that it not only effectively supports the current aviation safety mandatory report analysis work, but also can be applied to various data produced in the aviation safety field in the future.