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- 저자 : 최기쁨(Kippeum Choi) (검색결과 3 건)
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전자기록의 증거적 가치 수립을 위한 기능요건에 관한 연구: 미국 eDiscovery 적용을 중심으로
최기쁨,이젬마,오효정,Choi, Kippeum,Lee, Gemma,Oh, Hyo-Jung 한국정보관리학회 2021 정보관리학회지 Vol.38 No.2
근래까지 증거개시의 대상은 본래 종이 문서로 제한되어 왔으나, 디지털 시대로의 전환에 발맞추어 증거개시의 대상도 확대됨에 따라 현 시점에서는 전자적으로 생성된 정보에 관한 쟁점을 해결하는 것이 소송에서 중요해졌다. 따라서 본 연구에서는 전자기록이 증거로서 인정받기 위한 각 영역의 요건을 분석하고 기록관리 관점에서 고려해야 하는 요소를 설명하였다. 특히, 증거로서 전자기록을 활용되기 위한 선진 사례로, 미국의 전자증거개시제도인 eDiscovery를 선정하여 전자적 정보가 어떻게 수집, 보존되어 최종적으로 법원에 제출되는지를 살펴보았다. 특히 증거 보존 조치 지침을 조사하여 기록이 증거로서 보존되기 위해 고려되어야 하는 요건이 무엇인지 파악하였으며, 증거력이 있는 전자기록을 제출할 수 있도록 도와주는 도구인 eDiscovery 솔루션의 기능을 분석하였다. 본 연구결과를 통해 전자증거개시를 지원하는 시스템 구현 시 실질적인 도움이 될 수 있는 필수 기능요건을 도출하였다. Discovery's collection was originally paper documents, but with the advent of the digital age, its collection expanded. Resolving the issue of ESI has now become important in litigation. Therefore, this study analyzes the requirements of each domain for electronic records to be recognized as evidence. It also explained the factors that should be considered in record management. In addition, eDiscovery in the United States was selected as an advanced case to utilize records as evidence. This study investigated the Commentary on Legal Holdings: The Trigger & The Process and analyzed which functions must be considered in order for electronic records to be preserved as evidence. To this end, we analyze the functional requirements of the eDiscovery solution. Through this, necessary functional requirements are derived to help implement the system in preparation for eDiscovery.
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최기쁨(Kippeum Choi),유신성(Sin-Seong Yu),유남희(Nam-Hee Yoo),오효정(Hyo-Jung Oh) 한국정보기술학회 2021 한국정보기술학회논문지 Vol.19 No.2
With the recent creation of a new agricultural environment and changes in weather conditions, the need to predict and prevent pests has increased and many studies are being conducted. However, previous studies do not provide information intuitively and difficult to search for information on the user’s farm due to the wide range of pest prediction. For this reason, this study proposes a web visualization method of a pest prediction and prevention model based on farm-map data, which is an electronic map of agricultural land, so that users can easily search and consume information on the farm. This study utilized a pest prediction machine-learning model based on various public bigdata. It aims to visualize the disease and pest prediction and prevention model based on IPM(Integrated Pest Management) for ecological smart farm by providing information on appropriate methods to all other adjacent farms as well as the farm of the user.
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유신성(Sin-Seong Yu),최기쁨(Kippeum Choi),명현(Hyun Myung),오효정(Hyo-Jung Oh) 한국정보기술학회 2020 한국정보기술학회논문지 Vol.18 No.6
Pest managements are required preventive care before the outbreak. Recently, the correlation between pests and soil has been proved, and then it has beed increased the need for new predictive models different from existing studies. In this study, we propose prediction model of pest according to individual farms based on heterogeneous public big data. The model is focused on ‘prediction’ rather than existing ‘diagnosis’ research. In addition, we collected and refined information of various big data such as farmland, soil, pest historic records, as well as weather information, and finally utilized total of over 3.7 million data. Especially, the correlation of the collected factors was analyzed to select only the effective qualities for predicting pests. A two-step model was proposed for the prediction of pests in this study: The proposed model will be ultimately used as a preliminary study for the pest prediction and control system of ‘win-win’ using IPM(Integrated Pest Management).
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