• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.95-96
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• 2021

본 논문에서는, 서울시 자치구별 공공데이터를 활용한 분석 정보를 통해, 도시가 어떤 구조와 산업으로 형성되었는지 알아본다. 데이터 분석을 통해 얻어진 서울의 특징과 도시(자치구별)의 교통 측면, 상업, 데이터에서 발견한 정보를 통해 도시 특성과 구조를 알아본다. 본 논문에서 연구한 결과는 스마트 도시 정책에 활용하여 도시 기본 설계시 교통, 주거, 상업 등의 효율성을 증대 시키는데 기본 자료로 활용할 수 있다.

• The Journal of Bigdata
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• v.6 no.2
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• pp.61-70
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• 2021

The objective of this study was to describe specific approaches for data extraction from graph when statistical information is not directly reported in some articles, enabling data intergration and meta-analysis for quantitative data synthesis. Particularly, meta-analysis is an important analysis tool that allows the right decision making for evidence-based medicine by systematically and objectively selects target literature, quantifies the results of individual studies, and provides the overall effect size. For data integration and meta-analysis, we investigated the strength points about the introduction and application of Adobe Acrobet Reader and Python-based Jupiter Lab software, a computer tool that extracts accurate statistical figures from graphs. We used as an example data that was statistically verified throught an previous studies and the original data could be obtained from ClinicalTrials.gov. As a result of meta-analysis of the original data and the extraction values of each computer software, there was no statistically significant difference between the extraction methods. In addition, the intra-rater reliability of between researchers was confirmed and the consistency was high. Therefore, In terms of maintaining the integrity of statistical information, measurement using a computational tool is recommended rather than the classically used methods.

• Journal of Korean Society for Quality Management
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• v.50 no.4
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• pp.647-664
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• 2022

Purpose: The purpose of this study is to derive major policies that domestic small and medium-sized manufacturing companies should consider to maximize productivity and quality improvement by utilizing manufacturing data and AI, and to find priorities and implications. Methods: In this study, domestic and international issues and literature review by country were conducted to derive major considerations such as manufacturing AI technology, manufacturing AI talent, manufacturing AI data and manufacturing AI ecosystem. Additionally, the questionnaire survey targeting 46 experts of manufacturing data and AI industry were conducted. Finally, the major considerations and detailed factors importance were derived by applying the Analytic Hierarchy Process (AHP). Results: As a result of the study, it was found that 'manufacturing AI technology', 'manufacturing AI talent', 'manufacturing AI data', and 'manufacturing AI ecosystem' exist as key considerations for domestic manufacturing AI. After empirical analysis, the importance of the four key considerations was found to be 'manufacturing AI ecosystem (0.272)', 'manufacturing AI data (0.265)', 'manufacturing AI technology (0.233)', and 'manufacturing AI talent (0.230)'. The importance of the derived four viewpoints is maintained at a similar level. In addition, looking at the detailed variables with the highest importance for each of the four perspectives, 'Best Practice', 'manufacturing data quality management regime, 'manufacturing data collection infrastructure', and 'manufacturing AI manpower level of solution providers' were found. Conclusion: For the sustainable growth of the domestic manufacturing AI ecosystem, it should be possible to develop and promote manufacturing AI policies in a balanced way by considering all four derived viewpoints. This paper is expected to be used as an effective guideline when developing policies for upgrading manufacturing through domestic manufacturing data and AI in the future.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.6
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• pp.303-314
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• 2022

The persistence analysis of marine physical environment factors is a basic analysis that must precede the use of sea areas as an analysis required in the coastal engineering such as downtime and design. In this study, the persistence analysis was implemented for wind speed and significant wave height data from four observation points of Deokjeokdo, Oeyeondo, Geomundo, and Geojedo among the marine meteorological observation buoys of the Korea Meteorological Administration. The persistence time means the consecutive time of observation data beyond specific level. The threshold wind speed and significant wave height were set in the range of 1~15 m/s and the range of 0.25~3.0 m, respectively. Then, the persistence time was extracted. As a result of the analysis, the persistence time of wind speed and significant wave height decreased rapidly as the reference value increased. The median persistence times under the maximum reference thresholds were assessed as a maximum of 5 hours for wind speed and a maximum of 8 hours for significant wave height. When the reference wind speed and significant wave height were 15 m/s and 3 m, respectively, the persistence time that could occur with a 1% probability were 52 and 56 hours. This study can be expanded to all coastal areas in Korea, and it is expected that various engineering applications by performing a persistence analysis of the metocean data.

• The Journal of Bigdata
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• v.1 no.2
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• pp.9-19
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• 2016

Since ICT convergence became a major issue, German government has carried forward a policy 'Industry 4.0' that triggered ICT convergence with manufacturing. Now this trend gets into our stride. From this facts, we can expect great leap up to quality perfection in low cost. Recently Korean government also enforces policy with 'Manufacturing 3.0' for upgrading Korean manufacturing industry with being accelerated by many related technologies. We, in the paper, developed a custom-made operational big data analysis platform for the implementation of operational intelligence to improve industry capability. Our platform is designed based on spring framework and web. In addition, HDFS and spark architectures helps our system analyze massive data on the field with streamed data processed by process mining algorithm. Extracted knowledge from data will support enhancement of manufacturing performance.

• The Journal of Bigdata
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• v.1 no.2
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• pp.21-34
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• 2016

There are various investment strategy reports available online, prepared by many financial analysts. If the correlation between the title of the report and analyst forecast can be found, we can tell from the title whether analyst' forecast will be reliable or not. The objective of this study is to see the correlation between the title of analyst investment strategy report and the actual result of forecast by using the Text Mining technique. The result of actual analysis showed that "strong buy and sell call" appeared in the title lead the higher accuracy of analyst forecast and fulfillment ratio. The results that potential investors can get better information by reading the title of the analyst report. We hope that this study could be the basis for new methodologies in this area.

• Journal of Advanced Navigation Technology
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• v.16 no.3
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• pp.495-503
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• 2012

As modern society rapidly changes, the field of education has also developed speedily. Since Edunet system developed in 1996, many different systems are developing continuously such as Center for Teaching and Learning, cyber home learning systems, diagnosis prescribing systems, video systems, teaching and counseling, and study management systems. However, the aforementioned systems have had not great response from the educational consumers due to a lack of interconnection. There are several reasons for it. One of the reasons is that program administrators did not carefully consider the continuity of each programs but established a brand new system whenever they need rather than predict or consider the future needs. The suitable system for smart education should be one big integrated system based on many different data analysis and processing. The system should also supply educational consumers various and useful information by adopting the idea of bigdata rather than a single sign on system connecting each independent system. The cloud computing system should be established as a system that can be managed not as simple compiled files and application programs but as various contents and DATA.

• The Journal of Bigdata
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• v.2 no.1
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• pp.49-58
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• 2017

The inventory location is the mos important factor which decide the efficiency of picking orders. According to the inventory location, it is possible to optimize the route for picking order, and then it makes us to expect the cost reduction and efficiency improvement. However, it is practical situation to make decisions where to keep the products based on manager's intuition and experience, not based on the systematical or analytical approach. In this research, with the practical order data of cropper product and layout for the storage yard, the association rules have found, and then the new methodology has been devised to make the decision where to keep the inventory. By utilizing the practical order data for a year, it has been proved that the proposed approach can reduce the total distance of the all routes for picking order and solve the problem of delayed delivery.

• The Journal of Bigdata
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• v.2 no.1
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• pp.59-69
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• 2017

According to precedent research of disaster economics, most of the studies are either based on belated macroeconomic indicators or are limited to specific industries. It is certain that preventing disaster is important, but immediate analysis and reconstruction policy are crucial as well. This research analyzed the ripple effect of consumer spending followed by April 16 ferry disaster and MERS outbreak; it was done by applying credit card company's real-time big data with Marketing Mix Modeling. The main focus of this research is to see if it is possible to predict the scale of damage during ongoing disasters. It is found that setting up weekly MMM and moving the timeline draws significance conclusion. When disasters or events occur in future, this research may be the basis of building quick and intuitive indicator to monitor possible effects.

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