• Journal of Intelligence and Information Systems
• /
• v.25 no.2
• /
• pp.99-122
• /
• 2019

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 been nonexistent. This study also has a practical implication in that it can be a practical reference material for business decision makers considering big data investment.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.2
• /
• pp.841-854
• /
• 2020

The paper explains the method to process, analyze and predict traffic patterns in Los Angeles county using Big Data and Machine Learning. The dataset is used from a popular navigating platform in the USA, which tracks information on the road using connected users' devices and also collects reports shared by the users through the app. The dataset mainly consists of information about traffic jams and traffic incidents reported by users, such as road closure, hazards, accidents. The major contribution of this paper is to give a clear view of how the large-scale road traffic data can be stored and processed using the Big Data system - Hadoop and its ecosystem (Hive). In addition, analysis is explained with the help of visuals using Business Intelligence and prediction with classification machine learning model on the sampled traffic data is presented using Azure ML. The process of modeling, as well as results, are interpreted using metrics: accuracy, precision and recall.

• Journal of Korean Society for Quality Management
• /
• v.34 no.3
• /
• pp.51-61
• /
• 2006

This paper is dealing with a 6 sigma application in chemical industries. The company is enterprise which produce PR that is semiconductor material. CTQ is consisted of thickness (Big Y$_1$) and EOP (Big Y$_2$). After 6 sigma improvement activity that thickness (Big Y$_1$) improved from 0.98 sigma to 2.80 sigma and EOP (Big Y$_2$, energy optimizer) improved from 1.53 sigma to 3.98 sigma. The effectiveness of financial scope reduced 58,200,000 won of COPQ. But there are some problems to enforce 6 sigma in small enterprises. First, it is a lack of complete charge manpower enforcing S sigma activity. Second, it is a lack of professional knowledge of project leaders. Third, the passion of sponsorship (champion) is a lacking. Nevertheless useful tool was certified so that 6 sigma achieves quality reform in small enterprises.

• Journal of Digital Convergence
• /
• v.11 no.11
• /
• pp.435-441
• /
• 2013

This paper review about kerberos security authentication scheme and policy for big data service. It analyzed problem for security technology based on Hadoop framework in big data service environment. Also when it consider applying problem of kerberos security authentication system, it analyzed deployment policy in center of main contents, which is occurred in commercial business. About the related applied Kerberos policy in US, it is researched about application such as cross platform interoperability support, automated Kerberos set up, integration issue, OPT authentication, SSO, ID, and so on.

• Journal of Information Technology Applications and Management
• /
• v.24 no.3
• /
• pp.93-105
• /
• 2017

Demands of cosmetics are affected not only by the well-known elements such as brand, price, and customer's consumption capacity, but also by some latent factors, for example, weather and air environment. Due to complexity and dynamic changes of the above factors, their influences can hardly be estimated in an accurate way by the traditional approaches such as survey and questionnaires. Through modeling and statistical analysis of big data, this article studied the impacts of weather condition and air quality on customer flow and sales of the cosmetics distributors in China, and found several hidden influencing factors. It provided a big-data based method for the analysis of unconventional factors on cosmetics marketing in the changing weather condition and air environment.

• Quantitative Bio-Science
• /
• v.37 no.2
• /
• pp.91-101
• /
• 2018

This study has analyzed applicable characteristics on big data of the meteorology and climate depending on press releases in the media. As a result, more than half of them were conducted by governmental departments and institutions (26.9%) and meteorological administration (25.0%). Most articles were written by journalists, especially the highest portion stems from straight articles focusing on delivering simple information. For each field, the number of cases had listed in order of rank to be exposed to the media; information service, business management, farming, livestock, and fishing industries, and disaster management, but others did rank far behind; insurance, construction, hydrology and energy. Application of big data about meteorology and climate differed depending on the seasonal change, it was directly related to temperature information during spring, to weather phenomenon such as monsoon and heat wave during summer, to meteorology and climate information during fall, and to weather phenomenon such as cold wave and heavy snow during winter.

• International Journal of Advanced Culture Technology
• /
• v.12 no.2
• /
• pp.51-61
• /
• 2024

This study analyzes and compares the roles and significance of large firms in economic growth by differentiating developmental stages. The focus is on both the role of big businesses on the road from middle- to high-income countries and the performance in their economies. By classifying the top 30 nonfinancial firms into their origin countries, we have constructed a country-level data basis covering 33 countries ranging from middle- to high-income economies for the 2001 to 2017 period. We conduct fixed effect estimation. Empirical results show that capital-intensive big businesses would be more predominant in developed economies. In terms of policy implications, the results suggest that if policymakers want to optimize the role of big businesses in economic growth, policymakers need to distinguish the income level. Policymakers also need to adjust the size distribution of firms moderately ahead of time to create the size distribution of firms needed to take the economy to the next level.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.12
• /
• pp.716-722
• /
• 2016

In the recent rapidly changing IT environment, the amount of smart digital data is growing exponentially. As a result, in many areas, utilizing big data research and development services and related technologies is becoming more popular. In SMART learning, big data is used by students, teachers, parents, etc., from a perspective of the potential for many. In this paper, we describe big data and can utilize it to identify scenarios. Big data, obtained through customized learning services that can take advantage of the scheme, is proposed. To analyze educational big data processing technology for this purpose, we designed a system for big data processing. Education services offer the measures necessary to take advantage of educational big data. These measures were implemented on a test platform that operates in a cloud-based operations section for a pilot training program that can be applied properly. Teachers try using it directly, and in the interest of business and education, a survey was conducted based on enjoyment, the tools, and users' feelings (e.g., tense, worried, confident). We analyzed the results to lay the groundwork for educational use of big data.

• Journal of Fashion Business
• /
• v.11 no.3
• /
• pp.1-14
• /
• 2007

This research to reveal the foot types of old males consisted of two parts. First, a questionnaire was given for 180 old men in their 60s and above who live in Busan. Second, based on this survey on the reality of shoes wearing, direct and indirect measurement were held for 200 old gentlemen. The findings are as follows: 1. Survey Results of Shoes Wearing Reality In the investigation into the reality of shoes possession and wearing, most of old males favored active casual shoes with comfortable materials (40.8%). Hardened skin (23.6%) was the greatest in foot deformation and side effects resulting from shoes wearing, while the big toe (20.1%) was most uncomfortable. The greatest requirement for comfortable shoes was shoes making feet comfortable with a good sense of wear (41.0%), followed by shoes with the soft sole to absorb shock (31.7%), shoes with diverse sizes according to shoes width (13.7%), and shoes made of soft materials in consideration of various foot shapes. 2. Results of Foot Measurement Experiments Busan's males in their 60s and above were 166.31cm (Height), 63.51kg (weight), 23.94cm (foot length), 9.75cm (foot width), and 24.26cm (instep girth). The big toe angle of old males was $11.22^{\circ}$ and the little toe angle $14.70^{\circ}$. Four foot types were classified: 1 (long big foot), 2 (small inside-developed foot), 3 (toe-tip-gathered foot), and 4 (thin flat foot).

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.15 no.1
• /
• pp.1-7
• /
• 2019

The objective of this study is to examine the trends in big data. Research abstracts were extracted from 4,019 articles, published between 1995 and 2018, on Web of Science and were analyzed using topic modeling and time series analysis. The 20 single-term topics that appeared most frequently were as follows: model, technology, algorithm, problem, performance, network, framework, analytics, management, process, value, user, knowledge, dataset, resource, service, cloud, storage, business, and health. The 20 multi-term topics were as follows: sense technology architecture (T10), decision system (T18), classification algorithm (T03), data analytics (T17), system performance (T09), data science (T06), distribution method (T20), service dataset (T19), network communication (T05), customer & business (T16), cloud computing (T02), health care (T14), smart city (T11), patient & disease (T04), privacy & security (T08), research design (T01), social media (T12), student & education (T13), energy consumption (T07), supply chain management (T15). The time series data indicated that the 40 single-term topics and multi-term topics were hot topics. This study provides suggestions for future research.