• Journal of Korean Port Research
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• v.13 no.1
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• pp.101-112
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• 1999

Traffic accidents increase with the increase of the vehicles in operation on the street. Especially big traffic accidents composed of over 3 killed or 20 injured accidents with the property damage become one of the serious problems to be solved in most of the cities. The purpose of this study is to build the discrimination model on big traffic accidents using the Quantification II theory for establishing the countermeasures to reduce the big traffic accidents. The results are summarized as follows. 1)The existing traffic accident related model could not explain the phenomena of the current traffic accident appropriately. 2) Based on the big traffic accident types vehicle-vehicle, vehicle-alone, vehicle-pedestrian and vehicle-train accident rates 73%, 20.5% 5.6% and two cases respectively. Based on the law violation types safety driving non-fulfillment center line invasion excess speed and signal disobedience were 48.8%, 38.1% 2.8% and 2.8% respectively. 3) Based on the law violation types major factors in big traffic accidents were road and environment, human, and vehicle in order. Those factors were vehicle, road and environment, and human in order based on types of injured driver’s death. 4) Based on the law violation types total hitting and correlation rates of the model were 53.57% and 0.97853. Based on the types of injured driver’s death total hitting and correlation rates of the model were also 71.4% and 0.59583.

• Knowledge Management Research
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• v.21 no.4
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• pp.243-263
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• 2020

The purpose of this study is to propose the performance measurement model for open big data platform. In order to develop the performance measurement model, we have integrated big data reference architecture(NIST 2018) with performance prism model(Neely et al. 2001) in the platform perspective of open big data. Our proposed model consists of five key building blocks for measuring performance of open data platform as follows: stakeholder contribution, big data governance capabilities, big data service capabilities, big data IT capabilities, and stakeholder satisfaction. In addition, our proposed model have twenty four evaluation indices and seventy five measurement items. We believe that our model could offer both research and practical implications for relevant research.

• Journal of Digital Convergence
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• v.14 no.5
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• pp.95-106
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• 2016

The major thrust of this research focuses on the development of phased big data distribution model based on the big data ecosystem. This model consists of 3 phases. In phase 1, data intermediaries are participated in this model and transaction functions are provided. This system consists of general control systems, registrations, and transaction management systems. In phase 2, trading support systems with data storage, analysis, supply, and customer relation management functions are designed. In phase 3, transaction support systems and linked big data distribution portal systems are developed. Recently, emerging new data distribution models and systems are evolving and substituting for past data management system using new technology and the processes in data science. The proposed model may be referred as criteria for industrial standard establishment for big data distribution and transaction models in the future.

• Management & Information Systems Review
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• v.36 no.5
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• pp.85-98
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• 2017

The purpose of this study is to exploratory examine the structural relationships among meaningfulness of work, personality(Big 5 character-types) and job stress. To conduct such examination, the author (i) designated meaningfulness of work, personality(Big 5 character-types) and job stress as variables and (ii) designed a research model by conducting preceding studies on the variables. To examine the research model the author collected the survey data from the residents in Kyoungsangbuk-do, 332 copies of questionnaire. Collected data were analyzed using SPSS and AMOS programs. The analysis results are as follows. Especially, (1) the meaningfulness of work had a positive effect on agreeableness, conscientiousness, and extraversion. (2) the meaningfulness of work had a negative effect on neuroticism. (3) the meaningfulness of work had no effect on openness to experience. (4) the neuroticism factor had a positive effect on psychological job stress and physical job stress. (5) the openness to experience had a negative effect on psychological job stress and physical job stress. (6) the meaningfulness of work had no effect on psychological job stress and physical job stress. The implications and limitation which this study are as follows. First, this study has discovered that there was statistically significant relationship between the meaningfulness of work and Big 5 character-types. Second, Big 5 character-types(neuroticism, openness to experience) had statistically effect on psychological job stress and physical job stress. This study have limitation in that was conducted based on cross-sectional design of research. Because, the mechanism of job stress is a dynamic process.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.44-48
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• 2015

Housing price is a key indicator of housing demand. Actual Transaction Price Index of Apartment (ATPIA) released by Korea Appraisal Board is useful to understand the current level of housing price, but it does not forecast future prices. Big data such as the frequency of internet search queries is more accessible and faster than ever. Forecasting future housing demand through big data will be very helpful in housing market. The objective of this study is to develop a forecasting model of ATPIA as a part of forecasting housing demand. For forecasting, a concept of time shift was applied in the model. As a result, the forecasting model with the time shift of 5 months shows the highest coefficient of determination, thus selected as the optimal model. The mean error rate is 2.95% which is a quite promising result.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.537-544
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• 2005

정보화 환경에 적합한 인재의 Personality 유형 분류 및 주요 특징 분석을 통하여 이에 대한 모델을 제시하는 것이 본 연구의 목적이다. 기존 심리검사 및 정보화 성격 관련 연구의 한계점을 제시하고 조직 내 각 계층의 업무 수행에 필요한 성격 및 주요 성공 역할을 도출하여 이를 정보화 환경에 맞게 정리함으로써 정보화 성격 유형을 분류하고자 한다. 이러한 성격 유형들은 세부적으로 IT 창조자, Communicator, IT 리더, 정보 공유자, IT 감독자, 비전 제시자, 동기 부여자 등 7가지 수평적 유형으로 분류되며, 이러한 유형들의 분석을 위하여 성격 검사 연구인 Big 5 Model의 분석 방법 및 설문 문항을 적용하고자 한다. 이러한 정보화 성격 분류 및 각 유형에 대한 특성 제시는 개인의 정보화 성향 및 잠재성격을 파악하고 이를 개인적, 조직적으로 더욱 발전시킬 수 있는 방향을 제시할 수 있다.

• Journal of KIISE
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• v.44 no.1
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• pp.84-94
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• 2017

Recently, the importance of velocity, one of the characteristics of big data (5V: Volume, Variety, Velocity, Veracity, and Value), has been emphasized in the data processing, which has led to several studies on the real-time stream processing, a technology for quick and accurate processing and analyses of big data. In this paper, we propose a Squall framework using Time-triggered Message-triggered Object (TMO) technology, a model that is widely used for processing real-time big data. Moreover, we provide a description of Squall framework and its operations under a single node. TMO is an object model that supports the non-regular real-time processing method for certain conditions as well as regular periodic processing for certain amount of time. A Squall framework can support the real-time event stream of big data and micro-batch processing with outstanding performances, as compared to Apache storm and Spark Streaming. However, additional development for processing real-time stream under multiple nodes that is common under most frameworks is needed. In conclusion, the advantages of a TMO model can overcome the drawbacks of Apache storm or Spark Streaming in the processing of real-time big data. The TMO model has potential as a useful model in real-time big data processing.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.3
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• pp.167-183
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• 2017

The objective of this paper is to investigate the effect of character traits of employees of consulting firm on job satisfaction through self-leadership and self-efficacy. The proposed model is based on big5 model of Coasta and McCrae(1992). To validate the proposed model, structural equation model is analyzed with the valid 140 questionnaires collected from Seoul and nearby cities by using Smart PLS 3.0. The results are as follows. First, extraversion has a positive effect on self-leadership, not on self-efficacy. Second, agreeableness has a positive effect on self-leadership, not on self-efficacy. Third, conscientiousness has a positive effect on both self-leadership and self-efficacy. Fourth, emotional stability does not have a positive effect on both self-leadership and self-efficacy. Fifth, openness to experience has a positive effect on both self-leadership and self-efficacy. Sixth, self-leadership has a positive effect on self-efficacy. Seventh, self-leadership has a positive effect on job satisfaction. Last, self-efficacy has a positive effect on job satisfaction. This research proves that, while the character of employees generally forms the meaningful relationship with self-leadership and self-efficacy, in some entries of Big 5 character elements it has still produced the different results with the previous researches, which betrays that the relationship between one's character and self-leadership and self-efficacy can differ according to the characteristic of jobs. It requires further study to prove how each of Big 5 elements differently effects on self-leadership and self-efficacy according to diverse characteristic of jobs.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.551-571
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• 2023

Purpose: The purpose of this study is to propose useful suggestions by analyzing the causal effect relationship between the failure rate of quality and the process variables in the C5ISR domain of the defense industry. Methods: The collected data through the in house Systems were analyzed using Big data analysis. Data analysis between quality data and A/S history data was conducted using the CRISP-DM(Cross-Industry Standard Process for Data Mining) analysis process. Results: The results of this study are as follows: After evaluating the performance of candidate models for the influence of inspection data and A/S history data, logistic regression was selected as the final model because it performed relatively well compared to the decision tree with an accuracy of 82%/67% and an AUC of 0.66/0.57. Based on this model, we estimated the coefficients using 'R', a data analysis tool, and found that a specific variable(continuous maximum discharge current time) had a statistically significant effect on the A/S quality failure rate and it was analysed that 82% of the failure rate could be predicted. Conclusion: As the first case of applying big data analysis to quality issues in the defense industry, this study confirms that it is possible to improve the market failure rates of defense products by focusing on the measured values of the main causes of failures derived through the big data analysis process, and identifies improvements, such as the number of data samples and data collection limitations, to be addressed in subsequent studies for a more reliable analysis model.

• The Journal of Bigdata
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• v.4 no.1
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• pp.29-38
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• 2019

In this paper, we propose a sales forecasting model that forecasts the sales volume of short sleeves and outerwear according to the temperature change by utilizing accumulated big data from the online shopping mall 'A' over the past five years to increase sales volume and efficient inventory management. The proposed model predicts sales of short sleeves and outerwear according to temperature changes in 2018 by analyzing sales volume of short sleeves and outerwear from 2014 to 2017. Using the proposed sales forecasting model, we compared the sales forecasts of 2018 with the actual sales volume and found that the error rates are ±1.5% and ±8% for short sleeve and outerwear respectively.