• The Journal of Bigdata
• /
• v.2 no.2
• /
• pp.17-31
• /
• 2017

With the development of the big data environment, public institutions also have been providing big data infrastructures. Public data is one of the typical examples, and numerous applications using public data have been provided. One of the cases is related to the employment insurance. All employers have to make contracts for the employment insurance for all employees to protect the rights. However, there are abundant cases where employers avoid to buy insurances. To overcome these challenges, a data-driven approach is needed; however, there are lacks of methodologies to integrate, manage, and analyze the public data. In this paper, we propose a methodology to build a predictive model for identifying whether employers have made the contracts of employment insurance based on public data. The methodology includes collection, integration, pre-processing, analysis of data and generating prediction models based on process mining and data mining techniques. Also, we verify the methodology with case studies.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.29 no.7
• /
• pp.887-892
• /
• 2023

Vessel traffic service(VTS) centers are equipped with RADAR, AIS(Automatic Identification System), weather sensors, and VHF(Very High Frequency). VTS operators use this equipment to observe the movement of ships operating in the VTS area and provide information. The VTS data generated by these various devices is highly valuable for analyzing maritime traffic situation. However, owing to a lack of compatibility between system manufacturers or policy issues, they are often not systematically managed. Therefore, we developed the VTS Bigdata Platform that could efficiently collect, store, and manage control data collected by the VTS, and this paper describes its design and implementation. A microservice architecture was applied to secure operational stability that was one of the important issues in the development of the platform. In addition, the performance of the platform could be improved by dualizing the storage for real-time navigation information. The implemented system was tested using real maritime data to check its performance, identify additional improvements, and consider its feasibility in a real VTS environment.

• The Journal of Society for e-Business Studies
• /
• v.26 no.2
• /
• pp.61-81
• /
• 2021

In the era of untact, the "Chabak" using cars as accommodation spaces is attracting attention as a new form of travel. Due to the advantages, including low costs, convenience, and safety, as well as the characteristics of the vehicle enabling independent travel, the demand for Chabak is continuously increasing. Despite the rapid growth of the market and related industries, little academic has investigated this trend. To establish itself as a new type of travel culture and to sustain the growth of related industries, it is essential to understand the public perception of Chabak. Therefore, based on the marketing mix theory and big data analysis, this study analyzes the public perception of Chabak. The results showed that Chabak has established itself as a consumer-led travel culture, contributing to the aftermarket growth of the automobile industry. Additionally, consumers were found to be increasingly inclined to enjoy travel economically and wisely, and actively share information through social media. This initial study on the new travel trend of Chabak is significant in that it employs big data analysis on a theoretical basis.

• The Journal of Bigdata
• /
• v.4 no.1
• /
• pp.73-81
• /
• 2019

With the increase of education data, there have been many studies on the application of various analytics to improve students' performance and educational environments over the past decade. This paper first introduces the cases of universities that successfully utilize the analysis results and, more specifically, examines which data and analytical techniques are used for each analysis purpose. Based on the findings, the limitations of the current analytics and the direction of future analysis are discussed.

• The Journal of Bigdata
• /
• v.6 no.1
• /
• pp.161-178
• /
• 2021

The importance of 'Big Data' is increasing to the point that it is likened to '21st century crude oil'. For smart city IoT data, attention should be paid to quality control as the quality of data is associated with the quality of public services. However, data quality indicators presented through ISO/IEC organizations and domestic/foreign organizations are limited to the 'User' perspective. To complement these limitations, the study derives supplier-centric indicators and their priorities. After deriving 3 categories and 13 indicators of supplier-oriented smart city IoT data quality evaluation indicators, we derived the priority of indicator categories and data quality indicators through AHP analysis and investigated the feasibility of each indicator. The study can contribute to improving sensor data quality by presenting the basic requirements that data should have to individuals or companies performing the task. Furthermore, data quality control can be performed based on indicator priorities to provide improvements in quality control task efficiency.

• Communications of Mathematical Education
• /
• v.28 no.4
• /
• pp.573-588
• /
• 2014

How will the field of education react to the big data craze that has recently seeped into every aspect of society? To search for ways to use big data in mathematics education, this study first examined the concept of big data and examples of its application, and then pursued directions for future research in two ways. First, changes in the representation and acceptance of data are required because of changes in technology and the environment. In other words, the learning content and methodology of data treatment need to be changed by describing a myriad amount of data visually or by 'analyzing and inferring' data to provide data efficiently and clearly. Additionally, the mathematics education field needs to foster changes in curricula to facilitate the improvement of students' learning capacity in the 21st century. Second, it is necessary to more actively collect data on general education and not merely on teaching or learning to identify new information, pursue positive changes in the teaching and learning of mathematics, and stimulate interest and research in the field so that it can be used to make policy decisions regarding mathematics education.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.21 no.1
• /
• pp.57-70
• /
• 2018

The advancement and the spread of information and communication technology (ICT) changes the way we live and act. Computer and ICT devices become smaller and invisible, and they are now virtually everywhere in the world. Many socio-economic activities are now subject to the use of computer and ICT devices although we don't really recognize it. Various socio economic activities supported by digital devices leave digital records, and a myriad of these records becomes what we call'big data'. Big data differ from conventional data we have collected and managed in that it holds more detailed information of socio-economic activities. Thus, they offer not only new insight for our society and but also new opportunity for policy analysis. However, the use of big data requires development of new methods and tools as well as consideration of institutional issues such as privacy. The goals of this research are twofold. Firstly, it aims to understand the opportunities and challenges of using big data for planning support. Big data indeed is a big sum of microscopic and dynamic data, and this challenges conventional analytical methods and planning support tools. Secondly, it seeks to suggest ways of visualizing such spatial big data for planning support. In this regards, this study attempts to develop a dynamic visualization model and conducts an experimental case study with mobile phone big data for the Jeju island. Since the off-the-shelf commercial software for the analysis of spatial big data is not yet commonly available, the roles of open source software and computer programming are important. This research presents a pilot model of dynamic visualization for spatial big data, as well as results from them. Then, the study concludes with future studies and implications to promote the use of spatial big data in urban planning field.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.18 no.6
• /
• pp.1-9
• /
• 2018

In the fourth industrial revolution, which has become an issue now, we have been able to receive instant analysis results faster than the existing slow speed through various Big Data technologies, and to conduct real-time monitoring on mobile and web. First, various irregular sensor Data is generated using IoT device, Raspberry Pi. Sensor Data is collected in real time, and the collected data is distributed and stored using several nodes. Then, the stored Sensor Data is processed and refined. Visualize and output the analysis result after analysis. By using these methods, we can train the human resources required for Big Data and mobile related fields using IoT, and process data efficiently and quickly. We also provide information that can confirm the reliability of research results through real time monitoring.

• The Journal of Information Systems
• /
• v.32 no.4
• /
• pp.229-245
• /
• 2023

Purpose This study aims to understand the inclinations of young job seekers towards "hidden champions" - small but competitive companies that are emerging as potential solutions to the growing disparity between youth-targeted job vacancies and job seekers. We utilize machine learning techniques to discern the appeal of these hidden champions. Design/methodology/approach We examined the characteristics of small and medium-sized enterprises using data sourced from the Ministry of Employment and Labor and Youth Worknet. By comparing the efficacy of five machine learning classification models (i.e., Logistic Regression, Random Forest Classifier, Gradient Boosting Classifier, LGBM Classifier, and XGB Classifier), we discovered that the predictive model utilizing the LGBM Classifier yielded the most consistent performance. Findings Our analysis of the relative significance of preference determinants revealed that industry type, geographical location, and employee count are pivotal factors influencing preference. Drawing from these insights, we propose targeted strategic interventions for policymakers, hidden champions, and young job seekers.

• Review of KIISC
• /
• v.30 no.5
• /
• pp.101-109
• /
• 2020

온라인게임은 부정로그인 및 게임봇 (Game BOT) 탐지 등 서비스에 악영향을 주는 이상징후를 조기에 탐지해야 하는 서비스 분야이다 보니, 데이터기반 보안 (Data-Driven Security)이 상당히 오랜 기간 자생적으로 구축이 되어왔다. 온라인 게임은 초당 동시접속이 800만~1천만에 육박하는 게임도 시장에 빈번히 존재하기 때문에, 게임유저들의 로그데이터를 빅데이터 기술을 접목한 데이터 분석이 필수적이다. 본고에서는 온라인게임 분야에 존재하는 다양한 위협요소 중 하나인 게임봇 및 작업장 탐지에 적용된 데이터기반 보안 기술들에 대해 조사하고 향후 온라인게임분야에서의 데이터기반 보안의 연구 방향을 제시해 보고자 한다.