• Title/Summary/Keyword: Big data

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A Study on Priorities of the Components of Big Data Information Security Service by AHP (AHP 기법을 활용한 Big Data 보안관리 요소들의 우선순위 분석에 관한 연구)

  • Biswas, Subrata;Yoo, Jin Ho;Jung, Chul Yong
    • The Journal of Society for e-Business Studies
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    • v.18 no.4
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    • pp.301-314
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    • 2013
  • The existing computer environment, numerous mobile environments and the internet environment make human life easier through the development of IT technology. With the emergence of the mobile and internet environment, data is getting bigger rapidly. From this environment, we can take advantage of using those data as economic assets for organizations which make dreams come true for the emerging Big Data environment and Big Data security services. Nowadays, Big Data services are increasing. However, these Big Data services about Big Data security is insufficient at present. In terms of Big Data security the number of security by Big Data studies are increasing which creates value for Security by Big Data not Security for Big Data. Accordingly in this paper our research will show how security for Big Data can vitalize Big Data service for organizations. In details, this paper derives the priorities of the components of Big Data Information Security Service by AHP.

Challenges and Opportunities of Big Data

  • Khalil, Md Ibrahim;Kim, R. Young Chul;Seo, ChaeYun
    • Journal of Platform Technology
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    • v.8 no.2
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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.

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A Study on Big Data Analytics Services and Standardization for Smart Manufacturing Innovation

  • Kim, Cheolrim;Kim, Seungcheon
    • International Journal of Internet, Broadcasting and Communication
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    • v.14 no.3
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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.

Big Data Key Challenges

  • Alotaibi, Sultan
    • International Journal of Computer Science & Network Security
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    • v.22 no.4
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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.

An Analysis of Big Data Structure Based on the Ecological Perspective (생태계 관점에서의 빅데이터 활성화를 위한 구조 연구)

  • Cho, Jiyeon;Kim, Taisiya;Park, Keon Chul;Lee, Bong Gyou
    • Journal of Information Technology Services
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    • v.11 no.4
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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.

Big Data Analytics Case Study from the Marketing Perspective : Emphasis on Banking Industry (마케팅 관점으로 본 빅 데이터 분석 사례연구 : 은행업을 중심으로)

  • Park, Sung Soo;Lee, Kun Chang
    • Journal of Information Technology Services
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    • v.17 no.2
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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.

A Study on Policies to Revitalize the Public Big Data in Seoul (서울시 공공빅데이터 활성화 방안 연구)

  • Choi, Bong;Yun, Jongjin;Um, Taehyee
    • Knowledge Management Research
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    • v.20 no.3
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    • pp.73-89
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    • 2019
  • The purpose of this study is to investigate the current state of public Big Data in Seoul and suggest policy directions for the revitalization of Seoul's public Big Data. Big Data is perceived as innovation resources under the era of 4th Industrial revolution and Data economy. Especially, public Big Data serves a significant role in terms of universal access for citizens, startup, and enterprise compared with the private sector. Seoul reorganized a substructure of government's focus on Big Data and established organizations such as Big Data Campus and Urban Data Science Lab. Although the number of public open Data has increased in Seoul, there exists not much Data with characteristics similar to Big Data, such as volume, velocity, and value. In order to present the direction of Big Data policy in Seoul, we investigate the current status of Big Data Campus and Urban Data Science Lab operated by Seoul City. Considering the results of this study, we have proposed several directions that Seoul can use in establishing big data related strategies.

Big Data Smoothing and Outlier Removal for Patent Big Data Analysis

  • Choi, JunHyeog;Jun, Sunghae
    • Journal of the Korea Society of Computer and Information
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    • v.21 no.8
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    • pp.77-84
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    • 2016
  • In general statistical analysis, we need to make a normal assumption. If this assumption is not satisfied, we cannot expect a good result of statistical data analysis. Most of statistical methods processing the outlier and noise also need to the assumption. But the assumption is not satisfied in big data because of its large volume and heterogeneity. So we propose a methodology based on box-plot and data smoothing for controling outlier and noise in big data analysis. The proposed methodology is not dependent upon the normal assumption. In addition, we select patent documents as target domain of big data because patent big data analysis is a important issue in management of technology. We analyze patent documents using big data learning methods for technology analysis. The collected patent data from patent databases on the world are preprocessed and analyzed by text mining and statistics. But the most researches about patent big data analysis did not consider the outlier and noise problem. This problem decreases the accuracy of prediction and increases the variance of parameter estimation. In this paper, we check the existence of the outlier and noise in patent big data. To know whether the outlier is or not in the patent big data, we use box-plot and smoothing visualization. We use the patent documents related to three dimensional printing technology to illustrate how the proposed methodology can be used for finding the existence of noise in the searched patent big data.

The Analyzing Risk Factor of Big Data : Big Data Processing Perspective (빅데이터 처리 프로세스에 따른 빅데이터 위험요인 분석)

  • Lee, Ji-Eun;Kim, Chang-Jae;Lee, Nam-Yong
    • Journal of Information Technology Services
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    • v.13 no.2
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    • pp.185-194
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    • 2014
  • Recently, as value for practical use of big data is evaluated, companies and organizations that create benefit and profit are gradually increasing with application of big data. But specifical and theoretical study about possible risk factors as introduction of big data is not being conducted. Accordingly, the study extracts the possible risk factors as introduction of big data based on literature reviews and classifies according to big data processing, data collection, data storage, data analysis, analysis data visualization and application. Also, the risk factors have order of priority according to the degree of risk from the survey of experts. This study will make a chance that can avoid risks by bid data processing and preparation for risks in order of dangerous grades of risk.

A Study on the Factors Affecting the Decision Making Satisfaction and User Behavior of Big Data Characteristics (빅데이터 특성이 의사결정 만족도와 이용행동에 영향을 미치는 요인에 관한 연구)

  • Kim, Byung-Gon;Yoon, Il-Ki;Kim, Ki-Won
    • Journal of Information Technology Applications and Management
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    • v.28 no.1
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    • pp.13-31
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    • 2021
  • The purpose of this study is to find the factors that influence big data characteristics on decision satisfaction and utilization behavior, analyze the extent of their influence, and derive differences from existing studies. To summarize the results of this study, First, the study found that among the three categories that classify the characteristics of big data, qualitative attributes such as representation, purpose, interpretability, and innovation in the value innovation category greatly enhance decision confidence and decision effectiveness of decision makers who make decisions using big data. Second, the study found that, among the three categories that classify the characteristics of big data, the individuality properties belonging to the social impact category improve decision confidence and decision effectiveness of decision makers who use big data to make decisions. However, collectivity and bias characteristics have been shown to increase decision confidence, but not the effectiveness of decision making. Third, the study found that among the three categories that classify the characteristics of big data, the attributes of inclusiveness, realism, etc. in the integrity category greatly improve decision confidence and decision effectiveness of decision makers who make decisions using big data. Fourth, it was analyzed that using big data in organizational decision making has a positive impact on the behavior of big data users when the decision-making confidence and finally, decision-making effect of decision-makers increases.