• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1544-1553
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• 2018

Recently internal information leakage in industries is severely increasing in spite of industry security policy. Thus, it is essential to prepare an information leakage prevention measure by industries. Most of the leaks result from the insiders, not from external attacks. In this paper, a real-time internal information leakage prevention system via both storage and network is implemented in order to protect confidential file leakage. In addition, a Hadoop-based user behavior analysis and statistics system is designed and implemented for storing and analyzing information log data in industries. The proposed system stores a large volume of data in HDFS and improves data processing capability using RHive, consequently helps the administrator recognize and prepare the confidential file leak trials. The implemented audit system would be contributed to reducing the damage caused by leakage of confidential files inside of the industries via both portable data media and networks.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.211-213
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• 2013

In these days, the delusions of Big Data and apprehension about them are coming into the picture in many business fields. General techniques for preservation, analysis, and utilization of Big Data are falling short of useful techniques for the volume of fast-increasing data. However, there are some assertions that the power of analysis and prediction of Artificial Intelligence would intensify the power of Big Data analysis. This paper studies on business cases to try to graft the Artificial Intelligence technique onto Big Data analysis. We first research on various techniques of Artificial Intelligence and relations between Artificial Intelligence and Big Data. And then, we perform case studies of Big Data with using Artificial Intelligence and propose some roles of Big Data in the future.

• The Journal of Bigdata
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• v.7 no.2
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• pp.139-152
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• 2022

Engineering's work area for plants is a technical area that directly affects productivity, performance, and quality throughout the lifecycle from planning, design, construction, operation and disposal. Using the different types of data that occur to make decisions is important not only in the subsequent process but also in terms of cyclical cost reduction. However, there is a lack of systems to manage and analyze these integrated data. In this paper, we developed a knowledge base-based plant engineering analysis platform that can manage and utilize data. The platform provides a knowledge base that preprocesses previously collected engineering data, and provides analysis and visualization to use it as reference data in AI models. Users can perform data analysis through the use of prior technology and accumulated knowledge through the platform and use visualization in decision-support and systematically manage construction that relied only on experience.

• Convergence Security Journal
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• v.19 no.1
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• pp.121-126
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• 2019

The big data utilization category in the sports field was mainly focused on the big data analysis to improve the competence of the athlete and the performance. Since then, 'big data technology' which collect and analyze more detailed and diverse data through the application of ICT technology such as IoT and AI has been applied. The use of big data of sports contents in future has value and possibility in the smart environment, but it is necessary to overcome the shortage and limitation of platform to manage and share sports contents. In order to solve such problems, it is important to change the perception of the companies or providers that provide sports contents and cultivate and secure professional personnel capable of providing sports contents. Also, it is necessary to implement policies to systematically manage and utilize big data poured from sports contents.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.51-52
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• 2017

요우커를 중심으로 한 한류 열풍은 연예인, 미디어 콘텐츠 시장을 넘어서 한국산 제품에도 큰 영향을 미치고 있다. 특히, 화장품을 비롯한 뷰티 관련 제품은 가장 대표적인 시장으로 주목 받고 있다. 본 논문에서는 공공데이터 및 소셜 웹 데이터를 이용하여 화장품 관련 기업의 비즈니스를 위한 뷰티 트렌드 분석 서비스를 제안한다. 소셜 웹 데이터는 국내외 뷰티 시장에 대한 제품인지도를 중심으로 분석되며, 공공데이터는 국내에서 유통되는 화장품에 대한 안정성을 중심으로 분석한다.

• Journal of Korea Technology Innovation Society
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• v.11 no.2
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• pp.171-193
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• 2008

Every country of the world in the 21st century is going to push forward the policy protecting their intellectual property right, and international movement to protect their intellectual right is still more strengthened. Under these situations, the firm is required to seek for more efficient methods of technology security and business management while the existence of business itself is at risk. The control of intellectual properties in business should be considered in the patent management. In the company, the industrial property right should be into one of the management resource, and its importance has great influence on the existence of the company. The information in patent data can be used for strategic planning purposes. This study systematically evaluates the patenting behaviour of sample of 27 business firms within the Korean metal industry. Utilizing the patent application data between 2000 and 2005, the relationship between these patenting management and company performance is analysed. This study showed that patent activity per employee and labor productivity are positive relationship, but patent activity per employee and firm size are negative relationship. Since a positive relationship between patenting and company performance could be shown, patent activity gains important as a instrument for R&D planning.

• The Journal of Bigdata
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• v.7 no.1
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• pp.139-171
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• 2022

Big Data and the fourth industrial revolution are the first revolution that has not spawned a new form of energy but has triggered a new technological phenomenon called digitization. Digital transformation has caused disruptive innovation, and each country and major corporations need to respond to it. Despite this importance, empirical studies at home and abroad are insufficient. Therefore, in this study, factors affecting the promotion of corporate digital transformation were discovered through literature review, and a research model was developed and empirically analyzed by modifying and supplementing it through a Delphi study. The research model was composed of the main standards such as technology, innovation, organization, and environment and 17 sub-standards by combining the IDT and TOE models. In order to empirically analyze this, the AHP decision-making technique was used for experts in domestic digital transformation promotion companies and business partners. Companies that promote digital transformation will be able to increase the chances of achieving successful digital transformation if they take into account the factors that influence the digital transformation promotion according to the characteristics of the type of industry and company size of the group to which the company belongs.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.5
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• pp.236-244
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• 2006

Many attempts have been made to track the web usage patterns and provide suggestions that might help web operators get the information they need. These tracking mechanisms rely on mining web log files for usage patterns. The purpose of this study is to verify a web agent prototype that was built for mining web log files. The web agent for this paper was made by Java and ASP and the agent came into being as part of a cookie for a short-term data storage. For long-term data storage, the agent used a My-SQL as a Data Base. This agent system could inform that if the data comes from the web data mining agent, it could be a rapid information providing method rather than the case of data coming into a data mining tool. Therefore, the developed tool in this study will be helpful as a new kind of decision making system and expert system.

• Journal of Service Research and Studies
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• v.8 no.2
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• pp.41-65
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• 2018

With the dawn of society where individuals or enterprises based on data generate infinite profits, the significance of database operation management is growing centering on data quality. However, there are not many South Korean public or private entities managing them systematically. Against this backdrop, this study sought to investigate the current status and problems and explore how to improve from the perspective of auditors. To implement this study, audit checklist was improved and, based on it, auditors and IT experts were surveyed. The final data were analyzed to test the study hypotheses empirically. As a result of the analysis, it was found that the auditors had been highly satisfied with all of the items on the improved audit checklist for data quality-based database operation management. Moreover, non-auditors were also found to regard them within their acceptable range. This study is expected to help improve information system operation audit and enterprises data operation management.

• Journal of Korea Multimedia Society
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• v.12 no.1
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• pp.85-96
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• 2009

Due to the bitter competition in markets and the need changes of consumers, customer relationship management(CRM) plays a key role in determining management strategies in companies. In addition, due to the advance of Ubiquitous environment, new applications are developed in the CRM arena. Since traditional data gathering methods can invade people's privacy and cause inaccurate data, new methods are required. In this paper, we propose an RFID-based automatic analytical information generation system for a ubiquitous CRM. Firstly, we develop an RFID middleware. And then, we store the data acquired from the middleware into a database. Finally, we analyze the data automatically and convert the data into meaningful information. By applying our system to an exhibition hall, automatically generated analytical information are given to companies and customers. The proposed system can make many companies meet their customers' needs in a ubiquitous way and can give them more accurate data by using clustering, associating, sequencing when they make a decision for their successful marketing.