• 한국도서관정보학회지
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• 제49권3호
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• pp.331-359
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• 2018

이 연구의 목적은 Big6 모델과 Big6 수정 모델을 분석하여 Big6 모델의 특징을 찾고 현장에서 Big6 모델을 적용하는데 필요한 시사점을 도출하는 데 있다. 이를 위하여 AASL과 ACRL의 정보 리터러시 기준과 Big6 모델을 비교하였으며, 교육목표분류학에 영향을 받은 Big6 모델과 Big6+3 모델, Big8 모델 및 LG사이언스랜드에서 제공하는 Big6 모델을 분석하였다. 분석결과, Big6 모델은 정보 문제 해결 모델과 메타인지 활성화 전략 및 학생들의 정보 리터러시를 향상시키는 발판으로 활용이 가능하였으며 구성주의, 탐구기반 학습, 교육과정 통합, 협력교육, ICT기술 모델로 활용이 가능하였다. 비판적 사고능력 향상은 Big6 모델보다 사서교사나 사서의 Big6 모델의 적용방법과 관련이 있었다. 사서교사와 사서는 Big6 모델을 적용하기 위하여 교육과정을 체계적이고 구체적으로 계획할 필요가 있다.

• 한국전자거래학회지
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• 제18권4호
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• pp.301-314
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• 2013

IT기술의 발전은 기존의 컴퓨터 환경과 더불어 수많은 모바일 환경 및 사물 인터넷환경을 통해 사람의 삶을 편리하게 하고 있다. 이러한 모바일과 인터넷 환경의 등장으로 데이터가 급속히 폭증하고 있으며, 이러한 환경에서 데이터를 경제적인 자산으로 활용 가능한 Big Data 환경과 서비스가 등장하고 있다. 그러나 Big Data를 활용한 서비스는 증가하고 있지만, 이러한 서비스를 위해 발생되는 다량의 데이터에는 보안적 문제점이 있음에도 불구하고 Big Data의 보안성에 대한 논의는 미흡한 실정이다. 그리고 기존의 Big Data에 대한 보안적인 측면의 연구들은 Big Data의 보안이 아닌 Big Data를 활용한 서비스의 보안이 주를 이루고 있다. 이에 따라서 본 연구에서는 Big Data의 서비스 산업의 활성화를 위하여 Big Data의 보안에 대한 연구를 하였다. 세부적으로 AHP 기법을 활용한 Big Data 환경에서 보안관리를 위한 구성요소를 파악하고 그에 대한 우선순위를 도출하였다.

• Journal of Platform Technology
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• 제8권2호
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• pp.3-9
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• 2020

Big Data is a new concept in the global and local area. This field has gained tremendous momentum in the recent years and has attracted attention of several researchers. Big Data is a data analysis methodology enabled by recent advances in information and communications technology. However, big data analysis requires a huge amount of computing resources making adoption costs of big data technology. Therefore, it is not affordable for many small and medium enterprises. We survey the concepts and characteristics of Big Data along with a number of tools like HADOOP, HPCC for managing Big Data. It also presents an overview of big data like Characteristics of Big data, big data technology, big data management tools etc. We have also highlighted on some challenges and opportunities related to the fields of big data.

• International Journal of Internet, Broadcasting and Communication
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• 제14권3호
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• pp.91-100
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• 2022

Major developed countries are seriously considering smart factories to increase their manufacturing competitiveness. Smart factory is a customized factory that incorporates ICT in the entire process from product planning to design, distribution and sales. This can reduce production costs and respond flexibly to the consumer market. The smart factory converts physical signals into digital signals, connects machines, parts, factories, manufacturing processes, people, and supply chain partners in the factory to each other, and uses the collected data to enable the smart factory platform to operate intelligently. Enhancing personalized value is the key. Therefore, it can be said that the success or failure of a smart factory depends on whether big data is secured and utilized. Standardized communication and collaboration are required to smoothly acquire big data inside and outside the factory in the smart factory, and the use of big data can be maximized through big data analysis. This study examines big data analysis and standardization in smart factory. Manufacturing innovation by country, smart factory construction framework, smart factory implementation key elements, big data analysis and visualization, etc. will be reviewed first. Through this, we propose services such as big data infrastructure construction process, big data platform components, big data modeling, big data quality management components, big data standardization, and big data implementation consulting that can be suggested when building big data infrastructure in smart factories. It is expected that this proposal can be a guide for building big data infrastructure for companies that want to introduce a smart factory.

• 한국IT서비스학회지
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• 제17권2호
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• pp.207-218
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• 2018

Recently, it becomes a big trend in the banking industry to apply a big data analytics technique to extract essential knowledge from their customer database. Such a trend is based on the capability to analyze the big data with powerful analytics software and recognize the value of big data analysis results. However, there exits still a need for more systematic theory and mechanism about how to adopt a big data analytics approach in the banking industry. Especially, there is no study proposing a practical case study in which big data analytics is successfully accomplished from the marketing perspective. Therefore, this study aims to analyze a target marketing case in the banking industry from the view of big data analytics. Target database is a big data in which about 3.5 million customers and their transaction records have been stored for 3 years. Practical implications are derived from the marketing perspective. We address detailed processes and related field test results. It proved critical for the big data analysts to consider a sense of Veracity and Value, in addition to traditional Big Data's 3V (Volume, Velocity, and Variety), so that more significant business meanings may be extracted from the big data results.

• 한국IT서비스학회지
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• 제11권4호
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• pp.277-294
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• 2012

The purpose of this research is to analyze big data structure and various objects in big data industry based on ecological perspective. Big data is rapidly emerging as a highly valuable resource to secure competitiveness of enterprise and government. Accordingly, the main issues in big data are to find ways of creating economic value and solving various problems. However big data is not systematically organized, and hard to utilize as it constantly expands to related industry such as telecommunications, finance and manufacturing. Under this circumstance, it is crucial to understand range of big data industry and to which stakeholders are related. The ecological approach is useful to understand comprehensive industry structure. Therefore this study aims at confirming big data structure and finding issues from interaction among objects. Results of this study show main framework of big data ecosystem including relationship among object elements composing of the ecosystem. This study has significance as an initial study on big data ecosystem. The results of the study can be useful guidelines to the government for making systemized big data ecosystem and the entrepreneur who is considering launching big data business.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.340-350
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• 2022

The big data term refers to the great volume of data and complicated data structure with difficulties in collecting, storing, processing, and analyzing these data. Big data analytics refers to the operation of disclosing hidden patterns through big data. This information and data set cloud to be useful and provide advanced services. However, analyzing and processing this information could cause revealing and disclosing some sensitive and personal information when the information is contained in applications that are correlated to users such as location-based services, but concerns are diminished if the applications are correlated to general information such as scientific results. In this work, a survey has been done over security and privacy challenges and approaches in big data. The challenges included here are in each of the following areas: privacy, access control, encryption, and authentication in big data. Likewise, the approaches presented here are privacy-preserving approaches in big data, access control approaches in big data, encryption approaches in big data, and authentication approaches in big data.

• Fisheries and Aquatic Sciences
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• 제17권3호
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• pp.377-380
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• 2014

RNA interference (RNAi)-mediated transcriptional knock-down of Crassostrea gigas big defensin 1 and 2 genes (Cg-BigDef1 and Cg-BigDef2) was investigated. The cDNA sequences of Cg-BigDef1 and Cg-BigDef2 were identical, excluding an additional fragment of 20 nucleotides in Cg-BigDef1; thus, a long double-stranded RNA (dsRNA) targeting the mRNA of Cg-BigDef2 effectively downregulated both Cg-BigDef2 and Cg-BigDef1. In addition, long dsRNA targeting green fluorescent protein (GFP) did not affect transcription of the two big defensin genes. These results suggest that the transcriptional downregulation of Cg-BigDef1 and Cg-BigDef2 was mediated by sequence-specific RNA interference (RNAi). Despite injection of long dsRNA targeting Cg-BigDef2 into only the adductor muscle, knock-down of Cg-BigDef1 and Cg-BigDef2 was observed in the adductor muscle, hemocytes, mantle, and gills, suggestive of systemic spread of RNAi in C. gigas. Furthermore, the inhibitory effect of dsRNA persisted until 72 h post-injection, indicative of a long-lasting RNAi-mediated knock-down of target genes.

• 한국콘텐츠학회논문지
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• 제15권7호
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• pp.477-488
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• 2015

본 연구는 선행연구를 확장하여 비감사보수가 감사품질에 미치는 영향을 Big4 회계법인과 Non-Big4 회계법인으로 분류하여 분석하였다. 본 연구는 감사서비스와 비감사서비스를 동시에 제공받는 기업들만을 표본으로 선정하였으며 Big4와 Non-Big4 사이에 비감사보수가 감사품질에 미치는 영향에 대한 차이가 있는지를 집중하여 검증하였다. 분석결과 비감사보수는 Non-Big4 회계법인으로부터 감사 받는 기업들의 감사품질과만 유의적인 양(+)의 관련성이 있는 것으로 나타났다. 이는 지역 기업들을 주고객으로 삼고 있는 소형회계법인들의 경우 특정 기업에 대한 경제의존도가 높을 수 있고 따라서 비감사보수가 커질수록 독립성을 훼손할 유인이 크다고 해석할 수 있다. 한편 Big4 회계법인의 경우 비감사보수가 재량적 발생액 절댓값과 음(-)이 관련성이 없는 것으로 나타나 비감사서비스가 전문성을 강화시킨다는 근거는 찾을 수 없었다. 본 연구의 결과는 지속적으로 비감사서비스가 차지하는 비중이 커지는 현 시점에서 규제기관과 자본시장 참여자에게 유용한 통찰력을 제공할 것으로 기대된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권2호
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• pp.567-582
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• 2014

The purpose of this study is to qualitatively identify the typologies and characteristics of the big data marketing strategy in major companies that are taking advantage of the big data business in Korea. Big data means piles accumulated from converging platforms such as computing infrastructures, smart devices, social networking and new media, and big data is also an analytic technique itself. Numerous enterprises have grown conscious that big data can be a most significant resource or capability since the issue of big data recently surfaced abruptly in Korea. Companies will be obliged to design their own implementing plans for big data marketing and to customize their own analytic skills in the new era of big data, which will fundamentally transform how businesses operate and how they engage with customers, suppliers, partners and employees. This research employed a Q-study, which is a methodology, model, and theory used in 'subjectivity' research to interpret professional panels' perceptions or opinions through in-depth interviews. This method includes a series of q-sorting analysis processes, proposing 40 stimuli statements (q-sample) compressed out of about 60 (q-population) and explaining the big data marketing model derived from in-depth interviews with 20 marketing managers who belong to major companies(q-sorters). As a result, this study makes fundamental contributions to proposing new findings and insights for small and medium-size enterprises (SMEs) and policy makers that need guidelines or direction for future big data business.