• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.487-492
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• 2018

4차산업 혁명의 여파로 국내에서는 다양한 분야에 인공지능과 빅데이터 기술을 활용하여 이전에 시행 중인 다양한 서비스 분야에 기술적 접목과 보완을 시도하고 있다. 특히 금융권에서 자금을 빌린 기업들을 대상으로 여신 안정성을 확보하고 선제적인 대응을 위해 온라인 뉴스기사들과 SNS 데이터 등을 이용하여 부실가능성을 예측하고 실제 업무에 도입하려는 시도들이 국내 주요 은행들을 중심으로 활발히 진행 중이다. 우리는 국내의 국책은행에서 수행한 비정형 데이터 기반의 기업의 부실징후 예측 시스템 개발 과정에서 시도된 다양한 분석 방법과 결과 그리고 과정 중에 발생한 문제점들에 관해 기술하고 관련 이슈들에 관하여 다룬다. 결과적으로 본 논문은 레이블이 없는 대량의 기사들에 레이블을 달기 위한 자동 태거(tagger) 개발과 뉴스 기사 예측 결과로부터 부실 가능성을 예측하기 위한 모델 및 성능 면에서 기사 예측 정확도 92%(AUC 0.96) 및 부실 가능성 기업 예측에서도 정형 데이터 분석결과에 견줄만한 성과를 이루었고 이에 관해 보고한다.

• ETRI Journal
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• v.44 no.1
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• pp.135-146
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• 2022

The importance of big data analytics has become apparent with the increasing volume of data on the Internet. The amount of data will increase even more with the widespread use of Internet of Things (IoT). One of the most important application areas of the IoT is healthcare. This study introduces new real-time data analytics architecture for an IoT-based smart healthcare system, which consists of a wireless sensor network and a radio-frequency identification technology in a vertical domain. The proposed platform also includes high-performance data analytics tools, such as Kafka, Spark, MongoDB, and NodeJS, in a horizontal domain. To investigate the performance of the system developed, a diagnosis of Wolff-Parkinson-White syndrome by logistic regression is discussed. The results show that the proposed IoT data analytics system can successfully process health data in real-time with an accuracy rate of 95% and it can handle large volumes of data. The developed system also communicates with a riverbed modeler using Transmission Control Protocol (TCP) to model any IoT-enabling technology. Therefore, the proposed architecture can be used as a time-saving experimental environment for any IoT-based system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.317-320
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• 2016

The NH-NongHyup network and servers were paralyzed in 2011, in the 2013 3.20 cyber attack happened and Classified documents of Korea Hydro & Nuclear Power Co. Ltd were leaked on December in 2015. All of them were conducted by a foreign country. These attacks were planned for a long time compared to the script kids attacks and the techniques used were very complex and sophisticated. However, no successful solution has been implemented to defend an APT attack thus far. Therefore, we will use big data analytics to analyze whether or not APT attack has occurred in order to defend against the manipulative attackers. This research is based on the data collected through ISAC monitoring among 3 hierarchical Korean defense system. First, we will introduce related research about big data analytics and machine learning. Then, we design two big data analytics models to detect an APT attack and evaluate the models' accuracy and other results. Lastly, we will present an effective response method to address a detected APT attack.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.193-197
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• 2021

Customers are increasingly attracted towards different e-commerce websites and applications for the purchase of products significantly. This is the reason the sellers are moving to different internet based services to sell their products online. The growth of customers in this sector has resulted in the use of big data analytics to understand customers' behavior in predicting the demand of items. It uses a complex process of examining large amount of data to uncover hidden patterns in the information. It is established on the basis of finding correlation between various parameters that are recorded, understanding purchase patterns and applying statistical measures on collected data. This paper is a document of the bottom-up strategy used to manage the selling price of a first-time product for maximizing profit while selling it online. It summarizes how existing customers' expectations can be used to increase the sale of product and attract the attention of the new customer for buying the new product.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.150-162
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• 2022

This study investigated the impact of the latest developments in big data analytics capabilities (BDAC) on firm performance. The BDAC have the power to innovate existing management practices. Nevertheless, their impact on firm performance has not been fully is not yet fully elucidated. The BDAC relates to the flexibility of infrastructure as well as the skills of management and firm's personnel. Most studies have explored the phenomena from a theoretical perspective or based on factors such as organizational characteristics. However, this study extends the flow of previous research by proposing and testing a model which examines whether organizational exploration, exploitation and market agility mediate the relationship between the BDAC and firm performance. The proposed model was tested using survey data collected from the long-term employees over 10 years in 250 companies. The results analyzed through structural equation modeling show that a strong BDAC can help improve firm performance. An organization's ability to analyze big data affects its exploration and exploitation thereby affecting market agility, and, consequently, firm performance. These results also confirm the powerful mediating role of exploration, exploitation, and market agility in improving insights into big data utilization and improving firm performance.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1238-1249
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• 2020

Throughout the world, aging populations and doctor shortages have helped drive the increasing demand for smart healthcare systems. Recently, these systems have benefited from the evolution of the Internet of Things (IoT), big data, and machine learning. However, these advances result in the generation of large amounts of data, making healthcare data analysis a major issue. These data have a number of complex properties such as high-dimensionality, irregularity, and sparsity, which makes efficient processing difficult to implement. These challenges are met by big data analytics. In this paper, we propose an innovative analytic framework for big healthcare data that are collected either from IoT wearable devices or from archived patient medical images. The proposed method would efficiently address the data heterogeneity problem using middleware between heterogeneous data sources and MapReduce Hadoop clusters. Furthermore, the proposed framework enables the use of both fog computing and cloud platforms to handle the problems faced through online and offline data processing, data storage, and data classification. Additionally, it guarantees robust and secure knowledge of patient medical data.

• Journal of the Korea Computer Graphics Society
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• v.22 no.5
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• pp.27-36
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• 2016

As big data become more complex than ever, there has been a need for various techniques and approaches to better analyze and explore such big data. A research discipline of visual analytics has been proposed to help users' visual data analysis and decision-making. Since 2006 when the first symposium of visual analytics was held, the visual analytics research has become popular as the advanced technology in computer graphics, data mining, and human-computer interaction has been incorporated in visual analytics. In this work we introduce the visual analytics research by reviewing and surveying the papers published in IEEE VAST 2015 in terms of data and visualization techniques to help domestics researchers' understanding on visual analytics.

• Asia pacific journal of information systems
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• v.33 no.4
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• pp.1036-1042
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• 2023

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.2
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• pp.1-19
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• 2014

Recently, one of the core keywords in information technology (IT) as well as areas such as business management is big data. Big data is a term that includes technology, personnel, and organization required to gather/manage/analyze collection of data sets so large and complex that it becomes difficult to manage and analyze using traditional tools. The military has been accumulating data for a long period due to the organization's characteristic in placing emphasis on reporting and records. Considering such characteristic of the military, this study verifies the possibility of improving the performance of the military organization through use of big data and furthermore, create scientific development of operation, strategy, and support environment. For this purpose, the study organizes general status and case studies related to big data, traces back examples of data utilization by Korean's national defense sector through US military data collection and case studies, and proposes the possibility of using and applying big data in the national defense sector.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1129-1135
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• 2016

The NH-NongHyup network and servers were paralyzed in 2011, in the 2013 3.20 cyber attack happened and classified documents of Korea Hydro & Nuclear Power Co. Ltd were leaked on december in 2015. All of them were conducted by a foreign country. These attacks were planned for a long time compared to the script kids attacks and the techniques used were very complex and sophisticated. However, no successful solution has been implemented to defend an APT attacks(Advanced Persistent Threat Attacks) thus far. We will use big data analytics to analyze whether or not APT attacks has occurred. This research is based on the data collected through ISAC monitoring among 3 hierarchical Korean Defense System. First, we will introduce related research about big data analytics and machine learning. Then, we design two big data analytics models to detect an APT attacks. Lastly, we will present an effective response method to address a detected APT attacks.