• 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.

Study for Spatial Big Data Concept and System Building (공간빅데이터 개념 및 체계 구축방안 연구)

  • Ahn, Jong Wook;Yi, Mi Sook;Shin, Dong Bin
    • Spatial Information Research
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    • v.21 no.5
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    • pp.43-51
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    • 2013
  • In this study, the concept of spatial big data and effective ways to build a spatial big data system are presented. Big Data is defined as 3V(volume, variety, velocity). Spatial big data is the basis for evolution from 3V's big data to 6V's big data(volume, variety, velocity, value, veracity, visualization). In order to build an effective spatial big data, spatial big data system building should be promoted. In addition, spatial big data system should be performed a national spatial information base, convergence platform, service providers, and providers as a factor of production. The spatial big data system is made up of infrastructure(hardware), technology (software), spatial big data(data), human resources, law etc. The goals for the spatial big data system build are spatial-based policy support, spatial big data platform based industries enable, spatial big data fusion-based composition, spatial active in social issues. Strategies for achieving the objectives are build the government-wide cooperation, new industry creation and activation, and spatial big data platform built, technologies competitiveness of spatial 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.

An Assessment System for Evaluating Big Data Capability Based on a Reference Model (빅데이터 역량 평가를 위한 참조모델 및 수준진단시스템 개발)

  • Cheon, Min-Kyeong;Baek, Dong-Hyun
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.39 no.2
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    • pp.54-63
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    • 2016
  • As technology has developed and cost for data processing has reduced, big data market has grown bigger. Developed countries such as the United States have constantly invested in big data industry and achieved some remarkable results like improving advertisement effects and getting patents for customer service. Every company aims to achieve long-term survival and profit maximization, but it needs to establish a good strategy, considering current industrial conditions so that it can accomplish its goal in big data industry. However, since domestic big data industry is at its initial stage, local companies lack systematic method to establish competitive strategy. Therefore, this research aims to help local companies diagnose their big data capabilities through a reference model and big data capability assessment system. Big data reference model consists of five maturity levels such as Ad hoc, Repeatable, Defined, Managed and Optimizing and five key dimensions such as Organization, Resources, Infrastructure, People, and Analytics. Big data assessment system is planned based on the reference model's key factors. In the Organization area, there are 4 key diagnosis factors, big data leadership, big data strategy, analytical culture and data governance. In Resource area, there are 3 factors, data management, data integrity and data security/privacy. In Infrastructure area, there are 2 factors, big data platform and data management technology. In People area, there are 3 factors, training, big data skills and business-IT alignment. In Analytics area, there are 2 factors, data analysis and data visualization. These reference model and assessment system would be a useful guideline for local companies.

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.

Agriculture Big Data Analysis System Based on Korean Market Information

  • Chuluunsaikhan, Tserenpurev;Song, Jin-Hyun;Yoo, Kwan-Hee;Rah, Hyung-Chul;Nasridinov, Aziz
    • Journal of Multimedia Information System
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    • v.6 no.4
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    • pp.217-224
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    • 2019
  • As the world's population grows, how to maintain the food supply is becoming a bigger problem. Now and in the future, big data will play a major role in decision making in the agriculture industry. The challenge is how to obtain valuable information to help us make future decisions. Big data helps us to see history clearer, to obtain hidden values, and make the right decisions for the government and farmers. To contribute to solving this challenge, we developed the Agriculture Big Data Analysis System. The system consists of agricultural big data collection, big data analysis, and big data visualization. First, we collected structured data like price, climate, yield, etc., and unstructured data, such as news, blogs, TV programs, etc. Using the data that we collected, we implement prediction algorithms like ARIMA, Decision Tree, LDA, and LSTM to show the results in data visualizations.

Business Intelligence and Marketing Insights in an Era of Big Data: The Q-sorting Approach

  • Kim, Ki Youn
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.8 no.2
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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.