• The Journal of Bigdata
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• v.3 no.2
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• pp.87-99
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• 2018

This study analyze the factors affecting the number of bidders in public contracts by collecting contract data such as purchase of goods, service and facility construction through KONEPS among various forms of public contracts. The reason why the number of bidders is important in public contracts is that it can be a minimum criterion for judging whether to enter into a rational contract through fair competition and is closely related to the budget reduction of the ordering organization or the profitability of the bidders. The purpose of this study is to analyze the factors that determine the participation of bidders in public contracts and to present the problems and policy implications of bidders' participation in public contracts. This research distinguishes the existing sampling based research by analyzing and analyzing many contracts such as purchasing, service and facility construction of 4.35 million items in which 50,000 public institutions have been placed as national markets and 300,000 individual companies and corporations participated. As a research model, the number of announcement days, budget amount, contract method and winning bid is used as independent variables and the number of bidders is used as a dependent variable. Big data and multidimensional analysis techniques are used for survey analysis. The conclusions are as follows: First, the larger the budget amount of public works projects, the smaller the number of participants. Second, in the contract method, restricted competition has more participants than general competition. Third, the duration of bidding notice did not significantly affect the number of bidders. Fourth, in the winning bid method, the qualification examination bidding system has more bidders than the lowest bidding system.

• Proceedings of the CALSEC Conference
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• 2002.01a
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• pp.86-106
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• 2002

Informatization is a process that corporation's external environmental factors and internal environmental factors influence as complex. is a phenomenon that appears via this process. To evaluate that informatization was propeled well or informatization level is high can be dangerous work extremely by only once-over-lightly some factors, organization information ability is superior or infrastructure is constructed well. Therefore, an evaluation for industrial information systems that consider corporation's external environment and internal environment configurationally and objective estimation through this is required in national dimension. This research sorted types of business using types of business classification of 2001 EIII(Evaluation Indices of Industrial Informatization) laying stress on corporation's product and product production process for reflecting various industrial classification. And we are dividing whole our country corporations by manufacture industry, the construction industry, distribution industry, service industry, banking industry 5 types of business. To see such classed types industry classification from consistent viewpoint, we saw them within new framework, purchase, operation, physical distribution, marketing and sale. service etc. laying stress on primary businesses except support businesses of planning, financial management etc. To draw special quality of business center from primary business of each types of business, we draw industry classification Key Capability that centers when plans corporation's corporate strategy and information strategy. And we deducted industrial classification key production business connected with industry classification Key Capability. After drawing an evaluation items for industrial information systems in informatization analysis viewpoint laying stress on drawn businesses. Finally we did Case Study by making out an evaluation for industrial information systems questionnaire that considers special quality of manufacturing industry. Through EIII that consider the industrial classification, we could know that it explains the corporation's purchase, production, distribution in general and detail.

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

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.40 no.2
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• pp.1-13
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• 2022

This paper examined the history and development of Gyeongbokgung Palace's back garden based on historical materials and drawings such as Joseon Ilgi(Diaries of Joseon Dynasty), Joseon Wangjo Sillok(the Annals of the Joseon Dynasty), Doseongdaejido(the Great Map of Seoul), Bukgwoldohyeong(Drawing Plan of the Northern Palace), the Bukgung Palace Restoration Plan, Restoration Planning of Gyeongbokgung Palace and the following results were derived. First, it was confirmed that the Back Garden of Gyeongbokgung Palace was famous for its great location since the Goryeo Dynasty, and that it was named Namkyeong at that time and was a place where a shrine was built, and that castles and palaces were already built during the Goryeo Dynasty under the influence of Fengshui-Docham(風水圖讖) and Zhouli·Kaogongji(周禮考工記). Although the back garden of Gyeongbokgung Palace in the early Joseon Dynasty stayed out of the limelight as a back garden for the palace, it has a place value as a living space for the head of the state from King Gojong to the present. Second, in order to clearly identify the boundaries of back garden, through literature such as map of Doseongdo (Map of the Capital), La Coree, Gyeongmudae Area, Japanese Geography Custom Compendium, Korean Photo Album, JoseonGeonchukdoJip(The Illustration Book of Joseon Construction), Urban Planning Survey of Gyeongseong, it was confirmed that the current Blue House area outside Sinmumun Gate was built outside the precincts of Gyeongbokgung Palace. It was found that the area devastated through the Japanese Invasion of Korea in 1592, was used as a space where public corporations were combined through the process of reconstruction during the King Gojong period. In Japanese colonial era, the place value as a back garden of the primary palace was damaged, as the palace buildings of the back garden was relocated or destroyed, but after liberation, it was used as the presidential residence and restored the place value of the ruler. Third, in the back garden of Gyeongbokgung Palace, spatial changes proceeded through the Japanese Invasion and Japanese colonial era. The place with the greatest geographical change was Gyeongnongjae area, where the residence of the Japanese Government-General of Korea was built, and there were frequent changes in the use of the land. On the other hand, the current Gyeongmudae area, the forests next to the small garden, and the forests of Baekak were preserved in the form of traditional forests. To clarify this, 1:1200 floor plan of inner Gyeongmudae residence and satellite images were overlapped based on Sinmumun Gate, and as a result, it was confirmed that the water path originating from Baekak still exists today and the forest area did not change. Fourth, in the areas where the traditional forest landscape was inherited, the functional changes in the topography were little, and major old-age colonies are maintained. The old trees identified in this area were indicator tree species with historical value. Representatively, Pinus densiflora for. multicaulis Uyeki, located in Nokjiwon Garden, is presumed to have been preserved as one of Pinus densiflora for. multicaulis Uyeki planted next to Yongmundang, and has a historicality that has been used as a photo zone at dinners for heads of state and important guests. Lastly, in order to continuously preserve and manage the value of Gyeongbokgung Palace in Blue House, it is urgent to clarify the space value through excavation of historical materials in Japanese colonial era and establish a hierarchy of garden archaeology by era. In addition, the basis for preserving the historical landscape from the Joseon Dynasty to the modern era from Gyeongbokgung Palace should not damage the area of the old giant trees, which has been perpetuated since the past, and a follow-up study is needed to investigate all the forests in Blue House.