Handling Endogeneity Challenge in Big Astronomical Data

Sumedha Arora,PankajDeep Kaur 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.7

Using Big Data in statistically valid ways is posing a great challenge. The main misconception that lies in using Big Data is the belief that volume of data can compensate for any other deficiency in data. There is a need to use some standards and transparency when using Big Data in survey research. Certain surveys that are based on the Big Data tend to generate more complications and complexities in data such as some important variables tend to correlate with some errournious data. This correlation of data with residual noise causes the endogeneity problem. It is to be solved as a fact the main aim of research work is answering question which could only be done when data is fully analyzed. Through this we can utilize all available information. This paper throws light on addressing endogeneity particularly to the astronomical data set and also provides solutions and techniques for handling endogeneity in the respective data set. Finally it couples big data i.e. whole data of sky with the time domain.

빅데이터를 기반으로 한 경관디자인 국내 학위 연구 동향 분석

박혜경,이재호 한국공간디자인학회 2023 한국공간디자인학회논문집 Vol.18 No.7

(Background and Purpose) Research based on big data is being actively conducted in various fields such as cities, architecture, landscapes, and design. The scope of use of big data is gradually expanding, and the number of cases using big data is steadily increasing in the landscape design field, but trend analysis or trend research related to this is still insufficient. Therefore, this study aims to identify the types and analysis techniques of big data used according to the research characteristics and targets in the landscape design field by conducting a survey and trend analysis of studies using big data. Through this, the characteristics of big data analysis techniques that are highly utilized by type and field/target of research can be incorporated into the landscape design process, or it is intended to be a foundation study that can contribute to the insights or follow-up research necessary for the proposal of new research. (Method) This study conducted the first and second surveys that limit the categories to be investigated, targeting domestic master's and doctoral dissertations related to design using big data. In the first survey, seven words (design, landscape, city, architecture, product, vision, design + landscape) related to big data and design were combined to search for papers, and three words related to the main perspective of this study (city, landscape, design) were narrowed down, and the research fields of these studies and applied big data analysis techniques were identified. In the second survey, "methodology" and "process" were added and recombined into the subject word to extract research used in design based on the first survey. In this process, a total of 47 papers were identified, the final four were selected and the research contents were analyzed. (Results) The surveys confirmed that the interest and utilization of research using big data are continuously increasing in all areas of "landscape", "design", and "city". Text mining techniques were being used as the most basic method for big data analysis, and it was confirmed that certain phenomena were analyzed from various angles by using text mining in parallel or additional separate techniques depending on the subject. (Conclusions) Research in the field of landscape design using big data analysis techniques is expected to continue in the future. In particular, in the landscape design-related fields, the use of opinion mining (emotional analysis) was on the rise to solve user-centered problems, and need to revitalize various research that can discover new formativeness and aesthetics through emotion. In addition, if guidelines and processes are developed by applying and combining various big data techniques, the level of related fields is expected to increase, such as minimizing errors in carrying out certain tasks and securing quality above a certain level.

빅데이터 분석에 관한 마케팅적 접근

이서구(Seo Koo Lee) 대한경영학회 2015 大韓經營學會誌 Vol.28 No.1

빅데이터의 정책적 활용과 과제 : 빅데이터 추진 현황과 정책적 적용 쟁점을 중심으로

김성우,정건섭 한국비교정부학회 2014 한국비교정부학보 Vol.18 No.3

The study is concerned with policy implementation and alternative policy options of big data which has characteristics such as volume, velocity, and variability. Nowadays, what is so called, the issue of big data is ubiquitous in recent years, such as openness public data, solving alternatives of social problems using big data, etc. Especially, the role and issue of government will be focused on the application of big data. It is evident that scientific policy implementation and citizens’ administrative convenience should be improved by way of using big data technology in government. Unfortunately, there is no rule out that the gathering and analysis of unreliable big data will cause the error or noise of the policy. In order to reduce the error or noise of the big data related policy, it is necessary to have the expert or specialized person of big data more than anything else. There is no doubt that the number of experts of big data are equal to the regional competitive power in the near future. Notwithstanding, the infrastructure of Information Technology is inferior to the national capital regions; accordingly, the regional governments have to dependent on the central government in terms of the structure in system establishment, analysis, application of big data. This kind of disparity will cause the regional inequality in the era of big data. Therefore, the regional government should foster and support the IT or big data experts with the relation of the regional universities.

빅데이터의 법적 보호 문제 - 영업비밀보호법에 의한 보호 가능성을 중심으로 -

이일호 법조협회 2018 法曹 Vol.67 No.1

In the era of the 4th industrial revolution, the meaning and value of data have been emphasized drastically. In recent years we are also confronted with the new coined term “big data”, which is expected to overcome the limitations and demarcation experienced by the pre-existing data itself or databases that consist of them. As a result, the government and industry tend to be active in this area, either by investing in it or by utilizing it. The more frequently big data would be applied in industry, the more plausible there could exist disputes between entities with different interests. Thus it is requires legal discussion surrounding big data, already in the earlier stage. Among other legal aspects of big data, this Paper will focus on the possibility of its legal protection. To fulfil this aim, it firstly tries to define the concept of “big data” and extract some common characteristics of it by considering the current development in the relevant technologies. On the basis of this discussion, it is examined whether and how big data can claim protection under intellectual property law, expecially according to the protection regime sui generis for database investors which has been originally developed for typical databases in the traditional sense. However, it will more focus on the possible legal protection of big data as trade secret whose protection is governed in Korea by the Trade Secret Law within the context of the unfair competition regime. Rights arising from a trade secret are distinguished from exclusive rights conferred on holders of intellectual property and deem to be, as a consequence, characterized differently. It is difficult for the trade secret to identify and specify information in secret from the other. Furthermore, many scholars and practitioners still raise questions, whether claims based on the trade secret should be regarded as a right to the exclusive utilization or just a right to exclude someone from acquiring it. Such a uncertainty makes the trade secret law the last resort for businesses who would claim ownership over objects which has not been firmly recognized as an intellectual property yet. However, trade secret law has codified some liable conditions in depth for claiming protection, although they are sometimes considered insufficient and too abstract. Thus, the question which data can be protected under trade secret law depends on the interpretation of that law. The Paper will be concluded with the result: the trade secret law is not a suitable legal institute for the legal protection of big data. The reason is that the trade secret law is from the beginning not intended to protect data, and that it is not ready yet to recognize it as protectable. However, it is expected that big data would generate a huge amount of value and the investment would continue to increase. Answers to the questions remain still open, whether such information needs to be legally protected, and how and to what extend it can be protected, more importantly how we could formulate the legal language for the big data protection. It could be helpful to gather stakeholders, scholars and practitioners and to discuss on various issues around big data. 4차산업혁명의 시대를 맞이하여 데이터가 가지는 중요성은 줄곧 강조되고 있다. 최근에는 빅데이터(big data)라는 신조어가 나와 기존 데이터 또는 데이터베이스가 가지는 가능성을 뛰어 넘는 기술진보로서 각광을 받고 있으며, 그만큼 정부나 기업에 의한 활용도 활발해질 것으로 예상된다. 빅데이터가 빈번하게 사용되면, 그만큼 이와 관련된 분쟁이 벌어질 가능성도 높아지게 된다. 따라서 빅데이터를 둘러싼 법적 쟁점에 대해서 사전에 다루어 보는 것은 무척 중요한 일이라 하겠다. 이 논문은 빅데이터가 가지고 있는 법적 측면 중에서도 그 보호 가능성에 관해 고찰하기 위한 것이다. 논문은 우선 빅데이터를 어떻게 정의할 수 있는지, 또 현재의 발달상황을 고려해 보았을 때, 빅데이터가 가지고 있는 특징은 무엇인지 살펴보고자 한다. 이러한 분석을 토대로 빅데이터가 과연 현재의 지식재산권법(특히 저작권법) 및 불법행위법에 의해 보호될 수 있는지에 대해 고려해볼 것이다. 여기서는 무엇보다 부정경쟁방지 및 영업비밀보호에 관한 법률을 통해 빅데이터가 보호될 수 있는지에 대해 집중하고자 한다. 영업비밀은 여타 지식재산들과는 다른 성격을 가진다. 영업비밀로 보호되는 정보를 특정하기 어려운 것은 물론이고, 그 보호가 단지 방어권에 머무는지, 적극적인 이용권한에까지 이르는지에 대해 아직까지도 모호한 상황이다. 이러한 모호성 때문에 영업비밀보호법제는 때때로 명확하게 지식재산으로 보호되지 못하는 것을 보호하기 위한 마지막 보루처럼 여겨지기도 한다. 그러나 동 법제는 추상적이기는 하지만, 영업비밀로 보호되기 위해 갖추어야 할 요건들과 침해로 인정되는 행위를 비교적 구체적으로 제시하고 있다. 그 결과 빅데이터가 영업비밀이 되는지 여부는 이 조건들에 의해서 결정될 수밖에 없다. 결론적으로 영업비밀보호법제는 현재로서 빅데이터를 보호함에 있어 적합한 법제도라고 할 수는 없다. 이는 영업비밀보호법이 애초에 빅데이터를 전제로 만들어진 것이 아니기 때문이기도 하다. 빅데이터가 경제적 가치를 가지고, 산업계에서 그 가치를 인정받는 것은 사실이지만, 이것을 법적으로 보호할 것인지, 또 어떻게 얼마만큼 보호할 것인지는 또 다른 문제라고 할 수 있다. 이해관계를 달리하는 다양한 사람들, 법학자 및 법실무자들이 함께 모여 적합한 보호방안에 대해 심도 있는 논의를 전개할 필요가 있다.

빅데이터의 윤리적 활용을 위한 철학적 토대 : 역량 접근법을 중심으로

목광수 범한철학회 2019 汎韓哲學 Vol.95 No.4

인공지능 과학기술의 발전으로 인해, 빅데이터(Big Data)가 경제 발전의 원동력으로 주목받게 되었다. 그러나 빅데이터의 활용은 경제적 효용성뿐만 아니라, 프라이버시 침해와 같은 인간 가치의 훼손이라는 부작용 또한 상존한다. 따라서 빅데이터의 지속 가능한 활용을 위해서는 빅데이터가 윤리적으로 활용되어 정보 제공자들의 신뢰와 믿음을 얻을 필요가 있다. 왜냐하면, 빅데이터는 그 자체의 특성상, 시민들의 자발적 정보 제공에 의존하고 있다는 점에서 시민들의 지속적인 참여가 동반되어야지만 활용의 가치를 얻을 수 있기 때문이다. 본 논문은 빅데이터의 윤리적 활용을 위한 이론적 토대로 역량 접근법(capability approach)이 적합하다고 주장한다. 왜냐하면 역량 접근법은 빅데이터 활용의 윤리적 목표 설정을 위해 적합한 이론적 토대를 제공하며(2.1)절), 빅데이터라는 새로운 과학기술 논의를 포용할 수 있는 논의이기 때문이다(2.2)절). 더욱이 역량 접근법은 빅데이터 활용 과정에서 목표로 설정한 역량 증진을 도모하면서 나타날 수 있는 부작용, 예를 들면 프라이버시 침해 가능성과 같은 문제에 효과적으로 대응할 수 있는 이론적 토대를 제공하기 때문이다(3절). 본 논문은 역량 접근법이 빅데이터 활용 과정에서 나타나는 부작용에 효과적으로 대응하기 위해 개인적인 미시적 차원(3.1)절)과 사회적인 거시적 차원(3.2)절)의 통합적 구조를 제시할 수 있음을 보인다. 역량 접근법이 빅데이터의 윤리적 활용을 위한 철학적 토대로 제공되어 운용될 때, 빅데이터 기술은 인간의 가치를 고양할 수 있을 뿐만 아니라 정보 제공자인 시민들의 신뢰와 믿음을 통해 지속가능한 정보 제공이 가능해질 수 있을 것이다. For the sustainable use of Big Data, social trust and belief that Big Data is used ethically is essential. This is because Big Data in itself is dependent on the voluntary provision of personal data by citizens. For this reason, an ethical foundation to promote social trust of citizens is necessary for the sustainable use of Big Data. This paper argues that capability approach is appropriate as a theoretical framework for the ethical use of Big Data for three reasons. First, capability approach provides a suitable theoretical basis for setting ethical goals for the use of Big Data (Section 2.1)). Second, it can embrace the new science and technology discussion of Big Data (Section 2.2)). Third, capability approach can present an integrated structure of individual microscopic (Section 3.1) and social macroscopic dimensions (Section 3.2) to effectively cope with the side effects of using Big Data. When capability approach is provided and operated as a philosophical framework for the ethical use of Big Data, Big Data technologies can not only elevate human values, ​​but also can be sustainably used through the trust and trust of citizens as data providers.

Big Data Creation Process and Measures for Utilization: Focusing on the Transportation Sector

우정욱(Jungwouk WOO) 제주대학교 관광과경영경제연구소 2021 産經論集 Vol.41 No.3

Transportation big data is not limited to the transportation sector, but is a useful resource that will bring innovation to all aspects of our lives in the future, and various R&D for its utilization is currently in progress. However, the current level of utilization of transportation big data is very limited under the existing legal system. In this study, we will investigate the meaning and problems of the use of big data in the transportation sector, and investigate the improvement tasks to expand the use of big data. Research Design, Data and Methodology: The paper used a qualitative research methodology through the literature review. In this study, first, the definition and creation process of big data were studied. Second, the significance and problems of applying big data in the transportation sector were studied. Finally, the current status of research in the transportation sector using big data was investigated, and the tasks to be improved in the process from collecting transportation big data to analysis were reviewed. Results: Big data means creating new value by fusing data collected from different purposes. In the case of using big data, the transportation sector can establish more accurate and detailed transportation policies in basic data investigation, identification of phenomena, and prediction. In order to expand the use of big data, it is important to consider who owns it, what it was collected for, what the format of the collected data is, and what should be done to use it. Conclusion: Big data is a derivative thing, but it is becoming important enough to determine the success or failure of a country depending on how it is used. However, problems such as data errors or invasion of privacy that may occur when using big data are expected. This is not just a problem in the transportation sector. When using big data, there are many problems to be solved, such as data ownership, Big Brother problems, and the implementation of smart mobility. If the advent of the big data era is taken for granted, the task from now on is how to solve these problems and share their values.

기업의 빅데이터 투자가 기업가치에 미치는 영향 연구

권영진(Young jin Kwon),정우진(Woo-Jin Jung) 한국지능정보시스템학회 2019 지능정보연구 Vol.25 No.2

According to the recent IDC (International Data Corporation) report, as from 2025, the total volume of data is estimated to reach ten times higher than that of 2016, corresponding to 163 zettabytes. then the main body of generating information is moving more toward corporations than consumers. So-called “the wave of Big-data” is arriving, and the following aftermath affects entire industries and firms, respectively and collectively. Therefore, effective management of vast amounts of data is more important than ever in terms of the firm. However, there have been no previous studies that measure the effects of big data investment, even though there are number of previous studies that quantitatively the effects of IT investment. Therefore, we quantitatively analyze the Big-data investment effects, which assists firm’s investment decision making. This study applied the Event Study Methodology, which is based on the efficient market hypothesis as the theoretical basis, to measure the effect of the big data investment of firms on the response of market investors. In addition, five sub-variables were set to analyze this effect in more depth: the contents are firm size classification, industry classification (finance and ICT), investment completion classification, and vendor existence classification. To measure the impact of Big data investment announcements, Data from 91 announcements from 2010 to 2017 were used as data, and the effect of investment was more empirically observed by observing changes in corporate value immediately after the disclosure. This study collected data on Big Data Investment related to Naver ‘s’ News’ category, the largest portal site in Korea. In addition, when selecting the target companies, we extracted the disclosures of listed companies in the KOSPI and KOSDAQ market. During the collection process, the search keywords were searched through the keywords ‘Big data construction’, ‘Big data introduction’, ‘Big data investment’, ‘Big data order’, and ‘Big data development’. The results of the empirically proved analysis are as follows. First, we found that the market value of 91 publicly listed firms, who announced Big-data investment, increased by 0.92%. In particular, we can see that the market value of finance firms, non-ICT firms, small-cap firms are significantly increased. This result can be interpreted as the market investors perceive positively the big data investment of the enterprise, allowing market investors to better understand the company’s big data investment. Second, statistical demonstration that the market value of financial firms and non - ICT firms increases after Big data investment announcement is proved statistically. Third, this study measured the effect of big data investment by dividing by company size and classified it into the top 30% and the bottom 30% of company size standard (market capitalization) without measuring the median value. To maximize the difference. The analysis showed that the investment effect of small sample companies was greater, and the difference between the two groups was also clear. Fourth, one of the most significant features of this study is that the Big Data Investment announcements are classified and structured according to vendor status. We have shown that the investment effect of a group with vendor involvement (with or without a vendor) is very large, indicating that market investors are very positive about the involvement of big data specialist vendors. Lastly but not least, it is also interesting that market investors are evaluating investment more positively at the time of the Big data Investment announcement, which is scheduled to be built rather than completed. Applying this to the industry, it would be effective for a company to make a disclosure when it decided to invest in big data in terms of increasing the market value. Our study has an academic implication, as prior research looked for the impact of Big-data investment has bee

빅데이터 시대에 인문학의 역할과 과제

김연권 경기대학교 인문학연구소 2016 시민인문학 Vol.30 No.-

인터넷 기술과 정보통신의 기기의 발전으로 사물인터넷과 빅데이터의 시대가 열리게 되었다. 특히 경제적인 차원에서 빅데이터는 21세기의 원유로 불리고 있 으며, 구글이나 아마존 같은 회사들은 데이터 마이닝을 통해 엄청난 경제적 이익 을 추구하고 있다. 이 논문은 인문학적 차원에서도 빅데이터에 관심을 가질 필요 성이 있으며, 빅데이터를 경제적인 유용성과 활용성의 차원에서 뿐만 아니라 비 판적 성찰의 눈으로 바라보고자 한다. 이를 위해 이 논문은 4가지 질문을 제기하 고 그에 대한 답을 하는 방식으로 전개되어 있다. 첫째 질문은‘빅데이터와 사물인터넷 시대에 인간과 인문학은 어떤 도전에 직 면해 있는가?’이다. 데카르트 이후 몇 세기 동안 사유하는 주체로서의 인간의 이 미지는 확고한 것이었다. 그러나 사물인터넷 시대에 인간 존재는 사유하는 주체 로서보다 거대한 무형의 연결망 속에 포획당한 존재가 되어버렸다. 또한 빅데이 터 시대는 인간에 관한 모든 것 역시 데이터화된 정보로 수렴된다. 이런 점에서 빅데이터 시대에 인간은 디지털화된 정보의 양적인 총체로 전락될 우려가 있다. 두 번째 질문은‘빅데이터는 인문학 혹은 인문학자에게 어떤 활용가치가 있는 가?’이다. 실로 빅데이터는 인간과 세상에 대해 어떤 새로운 해석의 가능성을 열 어주는 열쇠가 될 수 있다. 빅데이터 분석을 통해 인문학자는 유한한 시간과 쓸 데없는 노동에서 해방되어 인간과 세계에 좀 더 거시적인 동시에 미시적인 통찰 력을 가질 수 있다. 세 번째 질문은‘인문학은 빅데이터 분석에서 어떤 긍정적 역할을 할 수 있는 가?’이다. 빅데이터는 다학제적인 연구이다. 흔히 전통적인 인문학자는 빅데이 터분석을 통계학이나 전산학의 영역으로 간주하여 크게 관심을 갖지 않는다. 그 러나 빅데이터 분석은 스토리텔링이나 언어학의 참여가 필요할 뿐만 아니라 폭 넓은 인문학적 소양을 필요로 한다. 네 번째 질문은 결론으로서‘빅데이터 시대에 한국의 인문학이 해야 할 일은 무엇인가?’이다. 한국의 인문학자는 우선 인문학적인 자료의 디지털화 작업에 더 욱 박차를 가해야 한다. 또한 인문학적 기반의 빅데이터 전문가 양성에 적극적으로 참여해야 한다. 아울러 다학제적인 빅데이터 연구에 적극적인 관심을 지니면 서 전통적인 인문학의 굴레에서 벗어나 인문학의 외연을 늘려야 한다. The era of big data and the Internet of Things have come due to the digital and communication revolution. Especially, big data is regarded as oil of 21th century and has increased the interest of information management companies such as Google, IBM, Oracle Corporation in terms of the economic aspect. However, the main purpose of this article is to examine the role and tasks of humanities in the era of big data. For this, this article brings out four questions and answers each question. First question is “What impact did the introduction of big data have on people’s worldview and on epistemology? The image of ‘le sujet pensant’of human beings has been solid since Descarthes’cogito. However, the human beings are captured as a knot water in enormous information network in the era of IOT and big data. Second question is“ What value does big data have for the field of humanities and the humanities major professors?”In fact, the analyse of big data can offer a new key that interprets deeply and exactly human beings and the world. In addition, the humanities scholars can be liberated from time restriction and physical labor such as data collection and analyse, and have more macroscopic and microscopic insight through the application of big data. Third question is“ How can the field of humanities or the humanities major professors contribute to the interpretation of big data? Big data is interdisciplinary science. Traditional humanities have very limited interest on big data because they regard big data as search domain of knowledge such as statistics and information technology. However, the analyse of big data requires broad liberal humanity mind as well as the collaboration of storytelling and linguistics As a conclusion, the last question is“ What are the current challenges faced by the Korean humanities? Korean scholars need to carry out more digitalization of korean humanities materials. In addition, they have to participate more actively in interdisciplinary study of big data and have more attention to train dataanalysts based on humanities.

스마트폰 CMF 디자인 개발에서 빅데이터(big data) 분석기술 활용방안 연구

오인균(Oh, In Kyun),김영미(Kim, Young Mi),차성욱(Cha, Sung Wook) 한국디지털디자인협의회 2014 디지털디자인학연구 Vol.14 No.4

빅데이터는 방대한 데이터 속에 숨겨진 정보를 찾아내는 것을 말하며, 현재 경제 트렌드에서 가장 많이 등장하는 키워드 중 하나이다. 경제 트렌드에서 빅데이터가 주요 키워드라면 디자인분야에서는 소비자 감성과 경험이 주요 키워드이며, 스마트폰 디자인에서는 이러한 소비자 감성과 경험을 CMF 디자인을 통해서 표현하고 있다. 따라서 본 연구는 CMF 디자인 개발에 이러한 빅데이터 활용방안을 제안하는 것을 목적으로 총 3단계의 과정을 거쳤다. 1단계에서는 문헌연구 중심으로 빅데이터에 대한 이론적 정의를 바탕으로 빅데이터를 적용한 디자인분야의 선행연구를 조사, 분석을 하였다. 또한 관련연구를 통하여 스마트폰 CMF 디자인개발 프로세스와 선행연구를 정리하였다. 2단계에서는 객관적인 연구결과 도출을 위하여 설문조사와 그룹인터뷰(FGI)를 실시하였다. 설문조사결과 CMF 디자이너들의 빅데이터에 대한 이해도는 중간정도였으며, 업무 활용가능성은 높게 생각하는 것으로 조사되었다. 또한 CMF 디자이너들은 프로세스에서 빅데이터 활용가능성이 제일 높은 단계로 Design Research단계를 뽑았다. 3단계에서는 이러한 조사결과와 그룹인터뷰(FGI)를 바탕으로 CMF 디자인개발에서의 빅데이터 활용방안을 작성하였다. 활용방안은 CMF 디자인개발 프로세스에서 각 단계별로 활용할 수 있는 빅데이터 분석기술을 접목하였다. 이러한 빅데이터 활용방안은 프로세스를 중심으로 구축되어 실무에서의 활용가능성을 높인 것이 장점이며, 이를 통해 업무 효율성을 높이고 좀 더 소비자들의 니즈를 담은 CMF 디자인 개발에 도움이 될 것으로 예상된다. Big data will find the information that was hidden in a large data. Big data is a keyword that appears the most in the current economic trends. If the big data is a major keyword in economic trends, experience and sensibility of the consumer is an important keyword in the design. The design of the smart phone, it have to express and experience sensibility through the CMF(Color, Material, Finishing). Therefore, we aim to develop CMF design, we propose a big data utilization of these methods, and through a process of three stages in this study. In first step, as a method of literature research, investigate the previous studies of big data applications and theoretical definition of big data and were analyzed. In addition, researchers was to organize the process of developing smart phone CMF design. In second step, researchers performed focus group interviews and questionnaire survey for the research results derived objective. It was found that the degree of understanding for the big data of CMF designer is moderate results of the survey, it is thought very likely take advantage of the business. In addition, CMF designer chose the Design Research step at the stage most likely of the big data utilized in the design process. In final step, based on the focus group interviews and survey results, we created a big data utilization plan of the development of CMF design. In the development process of CMF design, leverage proposal, a fusion of big data analysis technique which can be used at each step. We combine the ability to take advantage of each step in the design development process, CMF Utilization of Big Data analysis techniques. The advantage to this process is built around the Big Data Application of the enhanced availability of in practice. And we expected by this, increase the efficiency of business, and help in the development of CMF design that incorporates the needs of more consumers.