• Journal of the Korea Institute of Information Security & Cryptology
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• v.9 no.3
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• pp.13-26
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• 1999

Widespread use of Internet despite numerous positive aspects resulted in increased number of system intrusions and the need for enhanced security mechanisms is urgent. Systematic collection and analysis of log data are essential in intrusion investigation. Unfortunately existing logs are stored in diverse and incompatible format thus making an automated intrusion investigation practically impossible. We examined the types of log data essential in intrusion investigation and implemented a tool to enable systematic collection and efficient analysis of voluminous log data. Our tool based on RBDMS and SQL provides graphical and user-friendly interface. We describe our experience of using the tool in actual intrusion investigation and explain how our tool can be further enhanced.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.124-129
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• 2021

As the 4th industrial era is in full swing, the public's interest in related technologies such as artificial intelligence, big data, and block chain is increasing. As artificial intelligence technology is used in various industrial fields, the need for research methods incorporating artificial intelligence technology in related fields is also increasing. Evidence collection among digital forensic investigation techniques is a very important procedure in the investigation process that needs to prove a specific person's suspicions. However, there may be cases in which evidence is damaged due to intentional damage to evidence or other physical reasons, and there is a limit to the collection of evidence in this situation. Therefore, this paper we intends to propose an artificial intelligence-based evidence collection system that analyzes numerous image files reported by citizens in real time to visually check the location, user information, and shooting time of the image files. When this system is applied, it is expected that the evidence expected data collected in real time can be actually used as evidence, and it is also expected that the risk area analysis will be possible through big data analysis.

• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.1
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• pp.122-129
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• 2017

This study presents the results from a technical analysis of a portal system that is compatible with MRV regulations and utilized to examine energy efficiency in international shipping, in relation to the implementation of a mandatory data collection system by the International Maritime Organization. The details of the SEEMP guidelines, including the data collection system and methods for collecting data on fuel use, were reviewed. Strategies for domestic shipping companies toward MRV have been recommended by identifying differences with the EU MRV, and the technical adequacy of the MRV system was assessed. The MRV system enhances cost and work efficiency by managing emissions data from the early stage to the final stage. It is capable of collecting and reporting emissions data while adhering to the reporting procedures of shipping companies. By granting different access privileges to users, the system supports shipping companies in their data collection and reporting, and also supports verifiers in their data verification activities. Moreover, it makes possible the submission of reports in electronic from, thereby enabling shipping companies to adopt an integrated response to international MRV regulations.

• 시스템엔지니어링워크숍
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• s.4
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• pp.135-140
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• 2004

Defense analysis & evaluation includes menace analysis, validation analysis, problem analysis, scientific technical analysis, technical trad-off analysis, alternative analysis, cost analysis, etc. Reliable related data is required to perform these analysis activities efficiently. but in case of these defense analysis & evaluation data acquisition system, the data is insufficient and scattered about each organization. the data of database system is also not utilized sufficiently. abroad technical data is also low level data such as catalog or military officer's collection. therefore, this paper propose defense analysis & evaluation data acquisition system by systems engineering process. we also propose construction method of data acquisition system.

• Journal of the Korean Society of Systems Engineering
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• v.1 no.2
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• pp.69-76
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• 2005

Defense analysis & evaluation includes menace analysis, validation analysis, problem analysis, scientific technical analysis, technical trade-off analysis, alternative analysis, cost analysis, etc. Reliable related data is required to perform these analysis activities efficiently. but in case of these defense analysis & evaluation data acquisition system, the data is insufficient and scattered about each organization. The data of database system is also not utilized sufficiently. Abroad technical data is also low level data such as catalog or military officer's collection. Therefore, this paper propose defense analysis & evaluation data acquisition system by systems engineering process. we also propose construction method of data acquisition system.

• Convergence Security Journal
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• v.22 no.5
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• pp.69-80
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• 2022

Now that climate change and food resource security are becoming issues around the world, smart farms are emerging as an alternative to solve them. In addition, changes in the production environment in the primary industry are a major concern for people engaged in all primary industries (agriculture, livestock, fishery), and the resulting food shortage problem is an important problem that we all need to solve. In order to solve this problem, in the primary industry, efforts are made to solve the food shortage problem through productivity improvement by introducing smart farms using the 4th industrial revolution such as ICT and BT and IoT big data and artificial intelligence technologies. This is done through the public and private sectors.This paper intends to consider the minimum requirements for the smart farm data collection system for the development and utilization of smart farms, the establishment of a sustainable agricultural management system, the sequential system construction method, and the purposeful, efficient and usable data collection system. In particular, we analyze and improve the problems of the data collection system for building a Korean smart farm standard model, which is facing limitations, based on in-depth investigations in the field of livestock and livestock (pig farming) and analysis of various cases, to establish an efficient and usable big data collection system. The goal is to propose a method for collecting big data.

• Journal of the Korean Society for information Management
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• v.37 no.3
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• pp.25-50
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• 2020

Since big data platform services for the public library began January 1, 2016, libraries have used big data to improve their work performance. This paper aims to examine the use cases of library big data and attempts to draw improvement plan to improve the effectiveness of library big data. For this purpose, first, we examine big data used while utilizing the library big data platform, the usage pattern of big data and services/policies drawn by big data analysis. Next, the limitations and advantages of the library big data platform are examined by comparing the data analysis of the integrated library management system (ILUS) currently used in public libraries and data analysis through the library big data platform. As a result of case analysis, big data usage patterns were found program planning and execution, collection, collection, and other types, and services/policies were summarized as customizing bookshelf themes for the book curation and reading promotion program, increasing collection utilization, and building a collection based on special topics. and disclosure of loan status data. As a result of the comparative analysis, ILUS is specialized in statistical analysis of library collection unit, and the big data platform enables selective and flexible analysis according to various attributes (age, gender, region, time of loan, etc.) reducing analysis time. Finally, the limitations revealed in case analysis and comparative analysis are summarized and suggestions for improvement are presented.

• Quality Improvement in Health Care
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• v.5 no.2
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• pp.224-237
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• 1998

Background : This is an investigative study to analyse the importance of works perceived by Quality Improvement(QI) Coordinators and to evaluate their current work performances using the questionnaires developed by the investigator. Methods : The data were collected from 37 subjects over two weeks period from Oct. 1 to Oct. 17, 1998 and analysed by the descriptive statistics of SPSS program. The items of questionnaire are consisted of 13 work domains including 73 activities based on Quality Management(QM) Coordinator's job description of National Association for Healthcare Quality:data collection & analysis, communication, monitoring, evaluation, accreditation, tool development, policy development, program development, self development, education & trainning, system design, planning, and consultation & support. Results : 1) Of the performances in 13 work domains, the frequencies of the work performed were accreditation(89%), planning(88%), communication(83%), data collection & analysis(82%), monitoring(76%), policy development(72%), consultation & support (71%), education & trainning(70%), self development(68%), evaluation(63%), tool development(61%), program development(44%) and system design(43%) in order. 2) For the importances (1=not important, 5=very important), the policy development(4.46) scored highest then monitoring(4.42), planning(4.41), education & trainning(4.38), communication(4.35), evaluation(4.34) tool development (4.30), data collection & analysis(4.29), program development(4.22), consultation & support(4.22), accreditation(4.15), self development(4.05) and system design(3.98) in order. 3) There was a difference between the work performance and the perceived importance. The results showed the low performances in policy development, monitoring, education & trainning and evaluation which ranked high by the perceived importance and the high performances in accreditation, data collection & analysis, self development, communication and consultation & support which ranked middle to low by the perceived importance. 4) The reasons for low performances of QI Coordinators were the lack of clear assignment for the responsibility and allowed authority for work to QI Coordinators(30.8%), insufficient member of QI Coordinators(13.9%), lack of hospital director's interest(11.5%), low motivation of QI Coordinators (10.6%) and insufficient knowledge & experience of QI Coordinators (8.8%). Conclusion : Most works were perceived important by QI Coordinators, but there was a difference in the work performance. The works performed over 70% were related with accreditation, data collection & analysis, communication, planning and monitoring, on the other hand under 50% in performances were related with system design, program development, tool development and evaluation.

• Journal of the Korea Convergence Society
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• v.7 no.5
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• pp.1-6
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• 2016

Many studies have been carried out for the development of big data utilization and analysis technology recently. There is a tendency that government agencies and companies to introduce a Hadoop of a processing platform for analyzing big data is increasing gradually. Increased interest with respect to the processing and analysis of these big data collection technology of data has become a major issue in parallel to it. However, study of the collection technology as compared to the study of data analysis techniques, it is insignificant situation. Therefore, in this paper, to build on the Hadoop cluster is a big data analysis platform, through the Apache sqoop, stylized from relational databases, to collect the data. In addition, to provide a sensor through the Apache flume, a system to collect on the basis of the data file of the Web application, the non-structured data such as log files to stream. The collection of data through these convergence would be able to utilize as a basic material of big data analysis.

• Journal of the Korean Society of Industry Convergence
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• v.20 no.1
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• pp.67-73
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• 2017

This study aimed to analyze the fluid inside the ceramic filtration dust collection system which was assumed to be a stationary 3-dimensional turbulence. The fluid dynamics data necessary for performance curves were obtained based on the analysis results. The governing equations used to compute the velocity distribution and pressure inside the catalyst converter were expressed with continuity and momentum equations. Furthermore, the ${\kappa}-{\varepsilon}$ turbulence model, already validated by the industry(coal factory, high temperature dust collector) was used for the study. Of a total of three computational models employed, Model-1 served as the basis for CFD analysis which took measurements in increments of 70mm.