• 대한안전경영과학회지
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• 제14권4호
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• pp.229-238
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• 2012

Within last 10 years, internet has become a daily activity, and humankind had to face the Data Deluge, a dramatic increase of digital data (Economist 2012). Due to exponential increase in amount of digital data, large scale data has become a big issue and hence the term 'big data' appeared. There is no official agreement in quantitative and detailed definition of the 'big data', but the meaning is expanding to its value and efficacy. Big data not only has the standardized personal information (internal) like customer information, but also has complex data of external, atypical, social, and real time data. Big data's technology has the concept that covers wide range technology, including 'data achievement, save/manage, analysis, and application'. To define the connected technology of 'big data', there are Big Table, Cassandra, Hadoop, MapReduce, Hbase, and NoSQL, and for the sub-techniques, Text Mining, Opinion Mining, Social Network Analysis, Cluster Analysis are gaining attention. The three features that 'bid data' needs to have is about creating large amounts of individual elements (high-resolution) to variety of high-frequency data. Big data has three defining features of volume, variety, and velocity, which is called the '3V'. There is increase in complexity as the 4th feature, and as all 4features are satisfied, it becomes more suitable to a 'big data'. In this study, we have looked at various reasons why companies need to impose 'big data', ways of application, and advanced cases of domestic and foreign applications. To correspond effectively to 'big data' revolution, paradigm shift in areas of data production, distribution, and consumption is needed, and insight of unfolding and preparing future business by considering the unpredictable market of technology, industry environment, and flow of social demand is desperately needed.

• BMB Reports
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• 제50권1호
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• pp.12-19
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• 2017

Traditionally, biologists have devoted their careers to studying individual biological entities of their own interest, partly due to lack of available data regarding that entity. Large, high-throughput data, too complex for conventional processing methods (i.e., "big data"), has accumulated in cancer biology, which is freely available in public data repositories. Such challenges urge biologists to inspect their biological entities of interest using novel approaches, firstly including repository data retrieval. Essentially, these revolutionary changes demand new interpretations of huge datasets at a systems-level, by so called "systems biology". One of the representative applications of systems biology is to generate a biological network from high-throughput big data, providing a global map of molecular events associated with specific phenotype changes. In this review, we introduce the repositories of cancer big data and cutting-edge systems biology tools for network generation, and improved identification of therapeutic targets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권5호
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• pp.2029-2046
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• 2016

Energy consumption is an important index in mobile ad hoc networks. Data packet transmission increases among nodes, particularly in big data communication. However, nodes may be unable to transmit data packets because of energy over-consumption. Consequently, information may be lost and network communication may block. While opportunistic network is a kind of mobile ad hoc networks, researchers do not focus on energy consumption in opportunistic network communication. This study proposed an effective sensor energy supply scheme that can be applied in opportunistic networks. This scheme considers nodes sensor requests and communication model. In this scheme, nodes do not only accomplish energy supply in communication, but also extend communication time in opportunistic networks. Compared with the Spray and Wait algorithm and the Binary Spray and Wait algorithm in simulations, the proposed scheme extends communication time, increases data packet transmission, and accomplishes energy supply among nodes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권12호
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• pp.3416-3435
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• 2023

Task offloading in vehicular networks is hot topic in the development of autonomous driving. In these scenarios, due to the role of vehicles and pedestrians, task characteristics are changing constantly. The classical deep learning algorithm always uses a pre-trained neural network to optimize task offloading, which leads to system performance degradation. Therefore, this paper proposes a neural network fine-tuning task offloading algorithm, combining with location prediction for pedestrians and vehicles by the Payne model of fluid dynamics and the car-following model, respectively. After the locations are predicted, characteristics of tasks can be obtained and the neural network will be fine-tuned. Finally, the proposed algorithm continuously predicts task characteristics and fine-tunes a neural network to maintain high system performance and meet low delay requirements. From the simulation results, compared with other algorithms, the proposed algorithm still guarantees a lower task offloading delay, especially when congestion occurs.

• 한국조리학회지
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• 제24권3호
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• pp.167-176
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• 2018

In this study, Big Data was used to collect the information related to 'Cooking Academy' keywords. After collecting all the data, we calculated the frequency through the text mining and selected the main words for future data analysis. Data collection was conducted from Google Web and News during the period from January 1, 2013 to December 31, 2017. The selected 64 words were analyzed by using UCINET 6.0 program, and the analysis results were visualized with NetDraw in order to present the relationship of main words. As a result, it was found that the most important goal for the students from cooking school is to work as a cook, likewise to have practical classes. In addition, we obtained the result that SNS marketing system that the social sites, such as Facebook, Twitter, and Instagram are actively utilized as a marketing strategy of the institute. Therefore, the results can be helpful in searching for the method of utilizing big data and can bring brand-new ideas for the follow-up studies. In practical terms, it will be remarkable material about the future marketing directions and various programs that are improved by the detailed curriculums through semantic network of cooking school by using big data.

• 대한영양사협회학술지
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• 제22권4호
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• pp.310-319
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• 2016

Limitation of a questionnaire survey which is widely used is time and money, limited numbers of participants, biased confidence interval and unreliable results. To overcome these, we performed tendency and network analysis of diet using big Data in Koreans. The keyword on diet were collected from the portal site Naver from January 1, 2015 until December 31, 2015 and collected data were analyzed by simple frequency analysis, N-gram analysis, keyword network analysis and seasonality analysis. The results showed that diet menu appeared most frequently by N-gram analysis, even though exercise had the highest frequency by simple frequency analysis. In addition, keyword network analysis were categorized into four groups: diet group, exercise group, commercial diet program company group and commercial diet food group. The analysis of seasonality showed that subjects' interests in diet had increased steadily since February, 2015, although subjects were most interested indiet in July, these results suggest that the best strategies for weight loss are based on diet menu and starting diet before July. As people are especially sensitive to diet trends, researches are needed about annual analysis of big data.

• 한국정보시스템학회지:정보시스템연구
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• 제29권4호
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• pp.101-121
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• 2020

Purpose The purpose of this study is to understand the current status of MIS curriculum and to find ways to improve it. In addition, the results of the research can be used as basic data for improving MIS curriculum. Design/methodology/approach A research framework was designed to derive research results using the keyword network analysis method of this study: 1) Keywords were extracted based on the six units of the big data analysis job competency. 2) And based on the extracted keywords, the relationship between the keywords and MIS curriculum for each university was identified. Findings In the MIS curriculum information of a few universities, education related to big data analysis was conducted. 1) In the MIS curriculum of a few universities, education related to big data analysis was conducted. However, MIS curriculum of the university, which is the subject of analysis, education focused on concepts and theory rather than practical education was conducted. 2) And it was confirmed that there is a difference from the education required by the industry.

• Journal of Electrical Engineering and Technology
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• 제10권6호
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• pp.2384-2392
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• 2015

The technical development and practical applications of big-data for health is one hot topic under the banner of big-data. Big-data medical image fusion is one of key problems. A new fusion approach with coding based on Spherical Coordinate Domain (SCD) in Wireless Sensor Network (WSN) for big-data medical image is proposed in this paper. In this approach, the three high-frequency coefficients in wavelet domain of medical image are pre-processed. This pre-processing strategy can reduce the redundant ratio of big-data medical image. Firstly, the high-frequency coefficients are transformed to the spherical coordinate domain to reduce the correlation in the same scale. Then, a multi-scale model product (MSMP) is used to control the shrinkage function so as to make the small wavelet coefficients and some noise removed. The high-frequency parts in spherical coordinate domain are coded by improved SPIHT algorithm. Finally, based on the multi-scale edge of medical image, it can be fused and reconstructed. Experimental results indicate the novel approach is effective and very useful for transmission of big-data medical image(especially, in the wireless environment).

• Journal of Information Processing Systems
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• 제14권6호
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• pp.1464-1479
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• 2018

In face recognition, sometimes the number of available training samples for single category is insufficient. Therefore, the performances of models trained by convolutional neural network are not ideal. The small sample face recognition algorithm based on novel Siamese network is proposed in this paper, which doesn't need rich samples for training. The algorithm designs and realizes a new Siamese network model, SiameseFacel, which uses pairs of face images as inputs and maps them to target space so that the $L_2$ norm distance in target space can represent the semantic distance in input space. The mapping is represented by the neural network in supervised learning. Moreover, a more lightweight Siamese network model, SiameseFace2, is designed to reduce the network parameters without losing accuracy. We also present a new method to generate training data and expand the number of training samples for single category in AR and labeled faces in the wild (LFW) datasets, which improves the recognition accuracy of the models. Four loss functions are adopted to carry out experiments on AR and LFW datasets. The results show that the contrastive loss function combined with new Siamese network model in this paper can effectively improve the accuracy of face recognition.

• 지능정보연구
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• 제22권4호
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• pp.109-122
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• 2016

최근 빅데이터 분석은 기업과 전문가뿐만 아니라 개인이나 비전문가들도 큰 관심을 갖는 분야로 발전하였다. 그에 따라 현재 공개된 데이터 또는 직접 수집한 이터를 분석하여 마케팅, 사회적 문제 해결 등에 활용되고 있다. 국내에서도 다양한 기업들과 개인이 빅데이터 분석에 도전하고 있지만 빅데이터 공개의 제한과 수집의 어려움으로 분석 초기 단계에서부터 어려움을 겪고 있다. 본 논문에서는 빅데이터 공유를 방해하는 개인정보, 빅트래픽 등의 요소들에 대한 기존 연구와 사례들을 살펴보고 정책기반의 해결책이 아닌 시스템을 통해서 빅데이터 공유 제한 문제를 해결 할 수 있는 클라이언트-서버 모델을 이용해 빅데이터를 공개 및 사용 할 때 발생하는 문제점들을 해소하고 공유와 분석 활성화를 도울 수 있는 방안에 대해 기술한다. 클라이언트-서버 모델은 SPARK를 활용해 빠른 분석과 사용자 요청을 처리하며 Server Agent와 Client Agent로 구분해 데이터 제공자가 데이터를 공개할 때 서버 측의 프로세스와 데이터 사용자가 데이터를 사용하기 위한 클라이언트 측의 프로세스로 구분하여 설명한다. 특히, 빅데이터 공유, 분산 빅데이터 처리, 빅트래픽 문제에 초점을 맞추어 클라이언트-서버 모델의 세부 모듈을 구성하고 각 모듈의 설계 방법에 대해 제시하고자 한다. 클라이언트-서버 모델을 통해서 빅데이터 공유문제를 해결하고 자유로운 공유 환경을 구성하여 안전하게 빅데이터를 공개하고 쉽게 빅데이터를 찾는 이상적인 공유 서비스를 제공할 수 있다.