• 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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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.

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.

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.

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 in Smart Tourism: A Perspective Article

  • Park, Sangwon
    • Journal of Smart Tourism
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    • v.1 no.3
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    • pp.3-5
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    • 2021
  • The advancement of Information Communication Technology has provided tourism researchers with a golden opportunity to access big data, which plays a critical role in smart tourism. Recognizing the current issue, this paper discusses the evolution of the literature on tourism big data focusing on conceptual understanding of and types of big data, and insights from big data analytics. Indeed, this article provides important research agenda for future tourism researchers who would like to conduct academic research about big data and smart tourism.

Study on the Direction of Universal Big Data and Big Data Education-Based on the Survey of Big Data Experts (보편적 빅데이터와 빅데이터 교육의 방향성 연구 - 빅데이터 전문가의 인식 조사를 기반으로)

  • Park, Youn-Soo;Lee, Su-Jin
    • Journal of The Korean Association of Information Education
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    • v.24 no.2
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    • pp.201-214
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    • 2020
  • Big data is gradually expanding in diverse fields, with changing the data-related legislation. Moreover it would be interest in big data education. However, it requires a high level of knowledge and skills in order to utilize Big Data and it takes a long time for education spends a lot of money for training. We study that in order to define Universal Big Data used to the industrial field in a wide range. As a result, we make the paradigm for Big Data education for college students. We survey to the professional the Big Data definition and the Big Data perception. According to the survey, the Big Data related-professional recognize that is a wider definition than Computer Science Big Data is. Also they recognize that the Big Data Processing dose not be required Big Data Processing Frameworks or High Performance Computers. This means that in order to educate Big Data, it is necessary to focus on the analysis methods and application methods of Universal Big Data rather than computer science (Engineering) knowledge and skills. Based on the our research, we propose the Universal Big Data education on the new paradigm.

Providing Service Model Based on Concept and Requirements of Spatial Big Data (공간 빅데이터의 개념 및 요구사항을 반영한 서비스 제공 방안)

  • Kim, Geun Han;Jun, Chul Min;Jung, Hui Cheul;Yoon, Jeong Ho
    • Journal of Korean Society for Geospatial Information Science
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    • v.24 no.4
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    • pp.89-96
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    • 2016
  • By reviewing preceding studies of big data and spatial big data, spatial big data was defined as one part of big data, which spatialize location information and systematize time series data. Spatial big data, as one part of big data, should not be separated with big data and application methods within the system is to be examined. Therefore in this study, services that spatial big data is required to provide were suggested. Spatial big data must be available of various spatial analysis and is in need of services that considers present and future spatial information. Not only should spatial big data be able to detect time series changes in location, but also analyze various type of big data using attribute information of spatial data. To successfully provide the requirements of spatial big data and link various type of big data with spatial big data, methods of forming sample points and extracting attribute information were proposed in this study. The increasing application of spatial information related to big data is expected to attribute to the development of spatial data industry and technological advancement.