• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• 제17권5호
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• pp.187-203
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• 2022

As the economic, social, and environmental problems of the local community reach a serious level, our society is realizing the need to foster young talents who discover opportunities in local issues through entrepreneurship education and create social values through creative challenges. However, entrepreneurship education programs are generally focused on commerciality, so customized education programs to solve regional problems are insufficient. Therefore, this study aimed to develop a community problem-solving entrepreneurship curriculum. In this study, a competency based curriculum model was applied to develop the curriculum, and regional problem-solving entrepreneurship competencies were derived through expert advice from a total of 10 people. In the process, the Delphi methodology was additionally used to reduce the possibility of errors in the competency model. As a result of the study, a total of 23 regional problem-solving entrepreneurship competencies were confirmed, and knowledge(K) - skill(S) - attitude(A) by competency consisted of 5, 9, and 9, respectively. By applying this to Dunham's problem-solving six-step model, modular learning support measures were developed in the order of phase 1(problem discovery), phase 2(problem analysis), phase 3(plan), phase 4(measure), and phase 5(evaluation). This study is meaningful in that it integrated theory and practice by developing specific entrepreneurship curriculum and learning support measures based on the theoretical model devised in social welfare. In addition, it has implications in that it developed a regional problem-solving entrepreneurship competency model based on expert advice and proposed a specific curriculum based on this.

• Journal of Intelligence and Information Systems
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• 제23권1호
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• pp.1-22
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• 2017

Analysis of future business or investment opportunities, such as business feasibility analysis and company or technology valuation, necessitate objective estimation on the relevant market and expected sales. While there are various ways to classify the estimation methods of these new sales or market size, they can be broadly divided into top-down and bottom-up approaches by benchmark references. Both methods, however, require a lot of resources and time. Therefore, we propose a data-based intelligent demand forecasting system to support evaluation of new business. This study focuses on analogical forecasting, one of the traditional quantitative forecasting methods, to develop sales forecasting intelligence systems for new businesses. Instead of simply estimating sales for a few years, we hereby propose a method of estimating the sales of new businesses by using the initial sales and the sales growth rate of similar companies. To demonstrate the appropriateness of this method, it is examined whether the sales performance of recently established companies in the same industry category in Korea can be utilized as a reference variable for the analogical forecasting. In this study, we examined whether the phenomenon of "mean reversion" was observed in the sales of start-up companies in order to identify errors in estimating sales of new businesses based on industry sales growth rate and whether the differences in business environment resulting from the different timing of business launch affects growth rate. We also conducted analyses of variance (ANOVA) and latent growth model (LGM) to identify differences in sales growth rates by industry category. Based on the results, we proposed industry-specific range and linear forecasting models. This study analyzed the sales of only 150,000 start-up companies in Korea in the last 10 years, and identified that the average growth rate of start-ups in Korea is higher than the industry average in the first few years, but it shortly shows the phenomenon of mean-reversion. In addition, although the start-up founding juncture affects the sales growth rate, it is not high significantly and the sales growth rate can be different according to the industry classification. Utilizing both this phenomenon and the performance of start-up companies in relevant industries, we have proposed two models of new business sales based on the sales growth rate. The method proposed in this study makes it possible to objectively and quickly estimate the sales of new business by industry, and it is expected to provide reference information to judge whether sales estimated by other methods (top-down/bottom-up approach) pass the bounds from ordinary cases in relevant industry. In particular, the results of this study can be practically used as useful reference information for business feasibility analysis or technical valuation for entering new business. When using the existing top-down method, it can be used to set the range of market size or market share. As well, when using the bottom-up method, the estimation period may be set in accordance of the mean reverting period information for the growth rate. The two models proposed in this study will enable rapid and objective sales estimation of new businesses, and are expected to improve the efficiency of business feasibility analysis and technology valuation process by developing intelligent information system. In academic perspectives, it is a very important discovery that the phenomenon of 'mean reversion' is found among start-up companies out of general small-and-medium enterprises (SMEs) as well as stable companies such as listed companies. In particular, there exists the significance of this study in that over the large-scale data the mean reverting phenomenon of the start-up firms' sales growth rate is different from that of the listed companies, and that there is a difference in each industry. If a linear model, which is useful for estimating the sales of a specific company, is highly likely to be utilized in practical aspects, it can be explained that the range model, which can be used for the estimation method of the sales of the unspecified firms, is highly likely to be used in political aspects. It implies that when analyzing the business activities and performance of a specific industry group or enterprise group there is political usability in that the range model enables to provide references and compare them by data based start-up sales forecasting system.

• Journal of Intelligence and Information Systems
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• 제27권1호
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• pp.151-176
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• 2021

As the information technology industry develops, various kinds of data are being created, and it is now essential to process them and use them in the industry. Analyzing and utilizing various digital data collected online and offline is a necessary process to provide an appropriate experience for customers in the industry. In order to create new businesses, products, and services, it is essential to use customer data collected in various ways to deeply understand potential customers' needs and analyze behavior patterns to capture hidden signals of desire. However, it is true that research using data analysis and UX methodology, which should be conducted in parallel for effective service development, is being conducted separately and that there is a lack of examples of use in the industry. In thiswork, we construct a single process by applying data analysis methods and UX methodologies. This study is important in that it is highly likely to be used because it applies methodologies that are actively used in practice. We conducted a survey on the topic to identify and cluster the associations between factors to establish customer classification and target customers. The research methods are as follows. First, we first conduct a factor, regression analysis to determine the association between factors in the happiness data survey. Groups are grouped according to the survey results and identify the relationship between 34 questions of psychological stability, family life, relational satisfaction, health, economic satisfaction, work satisfaction, daily life satisfaction, and residential environment satisfaction. Second, we classify clusters based on factors affecting happiness and extract the optimal number of clusters. Based on the results, we cross-analyzed the characteristics of each cluster. Third, forservice definition, analysis was conducted by correlating with keywords related to happiness. We leverage keyword analysis of the thumb trend to derive ideas based on the interest and associations of the keyword. We also collected approximately 11,000 news articles based on the top three keywords that are highly related to happiness, then derived issues between keywords through text mining analysis in SAS, and utilized them in defining services after ideas were conceived. Fourth, based on the characteristics identified through data analysis, we selected segmentation and targetingappropriate for service discovery. To this end, the characteristics of the factors were grouped and selected into four groups, and the profile was drawn up and the main target customers were selected. Fifth, based on the characteristics of the main target customers, interviewers were selected and the In-depthinterviews were conducted to discover the causes of happiness, causes of unhappiness, and needs for services. Sixth, we derive customer behavior patterns based on segment results and detailed interviews, and specify the objectives associated with the characteristics. Seventh, a typical persona using qualitative surveys and a persona using data were produced to analyze each characteristic and pros and cons by comparing the two personas. Existing market segmentation classifies customers based on purchasing factors, and UX methodology measures users' behavior variables to establish criteria and redefine users' classification. Utilizing these segment classification methods, applying the process of producinguser classification and persona in UX methodology will be able to utilize them as more accurate customer classification schemes. The significance of this study is summarized in two ways: First, the idea of using data to create a variety of services was linked to the UX methodology used to plan IT services by applying it in the hot topic era. Second, we further enhance user classification by applying segment analysis methods that are not currently used well in UX methodologies. To provide a consistent experience in creating a single service, from large to small, it is necessary to define customers with common goals. To this end, it is necessary to derive persona and persuade various stakeholders. Under these circumstances, designing a consistent experience from beginning to end, through fast and concrete user descriptions, would be a very effective way to produce a successful service.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• 제14권1호
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• pp.117-137
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• 2019

In young entrepreneurs, the individual situation of opportunity discovery is very important. It is very important that the opportunities that are created for a particular individual entrepreneur are also recognized and assessed through the process. The need for the development of entrepreneurial opportunity competencies, which have a low proportion of opportunistic entrepreneurship, is low in the entrepreneurship education. In particular, young entrepreneurs are in desperate need of opportunistic entrepreneurship. The purpose of this study is to examine the effect of entrepreneurship motivation on entrepreneurial opportunity competence, using entrepreneurial orientation and entrepreneurship orientation as mediation variables for preliminary young entrepreneurs (19-39 old). In the case of young entrepreneurs, there is a tendency to study entrepreneurship policies and education through the system of youth entrepreneurship schools, mainly on college students and youths, and on the effects of institutional support on entrepreneurship. There is little research on the effect of a entrepreneurial motivation on the entrepreneurial opportunity competence needed to promote an entrepreneurial venture in a model with multiple mediators. The purpose of this study is to investigate the effect of start - up motivation on the entrepreneurial opportunity competence. To do this we analyzed 374 questionnaires collected from preliminary young entrepreneurs in Seoul and Gyeonggi provinces. The results of the analysis using SPSS v22.0 and Process macro v3.0 showed that the motivation of start - up had a significant effect on both opportunity recognition and opportunity evaluation of entrepreneurial opportunity competence. Second, motivation of entrepreneurs has a significant effect on entrepreneurial efficacy. Third, entrepreneurial efficacy has a significant effect on entrepreneurial orientation. Fourth, entrepreneurial orientation has a significant effect on entrepreneurial opportunity competence. Fifth, there is a significant indirect effect between entrepreneurial motivation and entrepreneurial opportunity recognition when passing through entrepreneurial orientation, entrepreneurial efficacy and entrepreneurial orientation at the same time, But indirect effects was insignificant when only entrepreneurship efficacy is passed. There is a significant indirect effect on all mediators between entrepreneurial motivation and entrepreneurial opportunity valuation. It is suggested that strengthening education on entrepreneurship is necessary to cultivate awareness of entrepreneurship opportunities and strengthening education on both entrepreneurial efficacy and entrepreneurship is necessary to cultivate evaluation of entrepreneurship opportunities by type of entrepreneurial motivation.

• Journal of KIISE:Databases
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• 제28권3호
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• pp.279-300
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• 2001

Data mining plays an important role in knowledge discovery process and usually various existing algorithms are selected for the specific purpose of the mining. Currently, data mining techniques are actively to the statistics, business, electronic commerce, biology, and medical area and currently numerous algorithms are being researched and developed for these applications. However, in a long run, only a few algorithms, which are well-suited to specific applications with excellent performance in large database, will survive. So it is reasonable to focus our effort on those selected algorithms in the future. This paper classifies about 30 existing algorithms into 7 categories - association rule, clustering, neural network, decision tree, genetic algorithm, memory-based reasoning, and bayesian network. First of all, this work analyzes systematic hierarchy and characteristics of algorithms and we present 14 criteria for classifying the algorithms and the results based on this criteria. Finally, we propose the best algorithms among some comparable algorithms with different features and performances. The result of this paper can be used as a guideline for data mining researches as well as field applications of data mining.

• Journal of Intelligence and Information Systems
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• 제25권3호
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• pp.19-41
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• 2019

According to the rapidly increasing demand for text data analysis, research and investment in text mining are being actively conducted not only in academia but also in various industries. Text mining is generally conducted in two steps. In the first step, the text of the collected document is tokenized and structured to convert the original document into a computer-readable form. In the second step, tasks such as document classification, clustering, and topic modeling are conducted according to the purpose of analysis. Until recently, text mining-related studies have been focused on the application of the second steps, such as document classification, clustering, and topic modeling. However, with the discovery that the text structuring process substantially influences the quality of the analysis results, various embedding methods have actively been studied to improve the quality of analysis results by preserving the meaning of words and documents in the process of representing text data as vectors. Unlike structured data, which can be directly applied to a variety of operations and traditional analysis techniques, Unstructured text should be preceded by a structuring task that transforms the original document into a form that the computer can understand before analysis. It is called "Embedding" that arbitrary objects are mapped to a specific dimension space while maintaining algebraic properties for structuring the text data. Recently, attempts have been made to embed not only words but also sentences, paragraphs, and entire documents in various aspects. Particularly, with the demand for analysis of document embedding increases rapidly, many algorithms have been developed to support it. Among them, doc2Vec which extends word2Vec and embeds each document into one vector is most widely used. However, the traditional document embedding method represented by doc2Vec generates a vector for each document using the whole corpus included in the document. This causes a limit that the document vector is affected by not only core words but also miscellaneous words. Additionally, the traditional document embedding schemes usually map each document into a single corresponding vector. Therefore, it is difficult to represent a complex document with multiple subjects into a single vector accurately using the traditional approach. In this paper, we propose a new multi-vector document embedding method to overcome these limitations of the traditional document embedding methods. This study targets documents that explicitly separate body content and keywords. In the case of a document without keywords, this method can be applied after extract keywords through various analysis methods. However, since this is not the core subject of the proposed method, we introduce the process of applying the proposed method to documents that predefine keywords in the text. The proposed method consists of (1) Parsing, (2) Word Embedding, (3) Keyword Vector Extraction, (4) Keyword Clustering, and (5) Multiple-Vector Generation. The specific process is as follows. all text in a document is tokenized and each token is represented as a vector having N-dimensional real value through word embedding. After that, to overcome the limitations of the traditional document embedding method that is affected by not only the core word but also the miscellaneous words, vectors corresponding to the keywords of each document are extracted and make up sets of keyword vector for each document. Next, clustering is conducted on a set of keywords for each document to identify multiple subjects included in the document. Finally, a Multi-vector is generated from vectors of keywords constituting each cluster. The experiments for 3.147 academic papers revealed that the single vector-based traditional approach cannot properly map complex documents because of interference among subjects in each vector. With the proposed multi-vector based method, we ascertained that complex documents can be vectorized more accurately by eliminating the interference among subjects.

• Journal of Intelligence and Information Systems
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• 제18권3호
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.

• Journal of Intelligence and Information Systems
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• 제20권1호
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• pp.177-193
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• 2014

Over the past decade, there has been a rapid diffusion of electronic commerce and a rising number of interconnected networks, resulting in an escalation of security threats and privacy concerns. Electronic commerce has a built-in trade-off between the necessity of providing at least some personal information to consummate an online transaction, and the risk of negative consequences from providing such information. More recently, the frequent disclosure of private information has raised concerns about privacy and its impacts. This has motivated researchers in various fields to explore information privacy issues to address these concerns. Accordingly, the necessity for information privacy policies and technologies for collecting and storing data, and information privacy research in various fields such as medicine, computer science, business, and statistics has increased. The occurrence of various information security accidents have made finding experts in the information security field an important issue. Objective measures for finding such experts are required, as it is currently rather subjective. Based on social network analysis, this paper focused on a framework to evaluate the process of finding experts in the information security field. We collected data from the National Discovery for Science Leaders (NDSL) database, initially collecting about 2000 papers covering the period between 2005 and 2013. Outliers and the data of irrelevant papers were dropped, leaving 784 papers to test the suggested hypotheses. The co-authorship network data for co-author relationship, publisher, affiliation, and so on were analyzed using social network measures including centrality and structural hole. The results of our model estimation are as follows. With the exception of Hypothesis 3, which deals with the relationship between eigenvector centrality and performance, all of our hypotheses were supported. In line with our hypothesis, degree centrality (H1) was supported with its positive influence on the researchers' publishing performance (p<0.001). This finding indicates that as the degree of cooperation increased, the more the publishing performance of researchers increased. In addition, closeness centrality (H2) was also positively associated with researchers' publishing performance (p<0.001), suggesting that, as the efficiency of information acquisition increased, the more the researchers' publishing performance increased. This paper identified the difference in publishing performance among researchers. The analysis can be used to identify core experts and evaluate their performance in the information privacy research field. The co-authorship network for information privacy can aid in understanding the deep relationships among researchers. In addition, extracting characteristics of publishers and affiliations, this paper suggested an understanding of the social network measures and their potential for finding experts in the information privacy field. Social concerns about securing the objectivity of experts have increased, because experts in the information privacy field frequently participate in political consultation, and business education support and evaluation. In terms of practical implications, this research suggests an objective framework for experts in the information privacy field, and is useful for people who are in charge of managing research human resources. This study has some limitations, providing opportunities and suggestions for future research. Presenting the difference in information diffusion according to media and proximity presents difficulties for the generalization of the theory due to the small sample size. Therefore, further studies could consider an increased sample size and media diversity, the difference in information diffusion according to the media type, and information proximity could be explored in more detail. Moreover, previous network research has commonly observed a causal relationship between the independent and dependent variable (Kadushin, 2012). In this study, degree centrality as an independent variable might have causal relationship with performance as a dependent variable. However, in the case of network analysis research, network indices could be computed after the network relationship is created. An annual analysis could help mitigate this limitation.

• Journal of Intelligence and Information Systems
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• 제21권1호
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• Journal of The Korean Society of Grassland and Forage Science
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• 제25권1호
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• pp.71-82
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• 2005

Khori(高麗) refers to the Chaabog(reindeer) that live on lichens(蘚) on Mt. Soyon(鮮) in which pastures are the cold and dry plateau of North Eurasia. Thus, the origin region of the Khori or Koguryo that are the ancestors of the reindeer-herding pastoral nomads(馴鹿 遊牧民) can be said to be the Steppe-Taiga-Tundra pastoral areas of North Eurasia and North America. When the pastoral nomads moved on to the great mountain(大山) zone of the Jangbaek(長白) to the Baekdu(白頭) Mountains, they could have been in contact with pastoral farmers or agricultural farmers living there and they became the farmers remaining on agricultural farms. They were the Koryo people, the ancestors of Korea. Staying in one place, they gradually forgot the origin of their reindeer-herding pastoral nomadic history in the Northwest area of Mt. Soyon, the small mountain(小山) zone of the Steppe-Taiga-Tundra pastoral areas. In other words, they lost their identity as reindeer-herding pastoral nomads when they entered the agricultural area after leaving the pastoral area. However, since their basic genes had already formed when they lived on the cold and dry plateau of North Eurasia, it is possible to study their pastoral nomadic history focusing on 'the minority living in the broad area(廣域少數)', by utilizing highly advanced biotechnological science and focusing on genes and information technology innovation, and removing various past hindrances in research. Therefore, it is not so difficult to restore the reindeerherding pastoral nomadic history of the Koguryo(高句麗) people and secure their pastoral nomadic identity, of which the first steps have already been taken into their historical stages. The Eurasian continent and the Korean peninsula, especially the cold and dry plateau of North Eurasia and the Korean peninsula have been closely related to each other ecologically and historically. They can never be a separate space at all. The Eurasian continent lies horizontally east to west and thus, the continent forms an isothermal zone. Also, since the time of producing their own foods, it was relatively easy for people with their technology to move to other places owing to the pastoral nomadic characteristic of mobility. Unlike the Chungyen(中原) region, western Asia and the regions covering the Siberia-Manchu-Korean peninsula where food production revolution was first made were connected to the Mongolian lichens route(蘚苔之路: Ni, ukinii jam) and steppe roads. Although the ecological conditions of nature have changed a bit throughout a long history, it was natural for the many tribes in North Asia living on the largest Steppe-Taiga-Tundra area in the world to have believed 'the legends related to animals in relation to their founders and ancestors(獸祖傳說)'. Assuming that Siberian tigers and the tigers living on Mt. Baekdu were connected ecologically and genetically because of the ecological characteristics of the animals, and their migration from plateau to plateau, we would suspect that the Chosun(朝鮮) tribe living on Mt. Baekdu were ethnically and culturally more closely connected to the farther removed Ural-Altai tribes that lived on the cold and dry plateau region than to the Han(i14;) tribe who lived in Chungyen(中原) that was close to Mt. Baekdu. More evidence is the structure of the Korean language which has the form of 'Subject + Object + Verb', which is assumed to have originated from the speedy lifestyle of the reindeer-herding pastoral nomads. The structure is quite different from that of the Han(漢) language, which is based on agricultural life. Also, it is natural for reindeer riding reindeerherding pastoral nomads or horse-riding sheep-herding pastoral nomads(騎馬, 羊遊牧民) to have held military and political power over the region and eventually to have established an ancient pastoral nomadic empire in the process of their conquest of agricultural regions. The stages for founding global empires in the history of mankind maybe largely divided into two, in terms of ecological conditions and occupations. They are the steppes and the oceans. Of course, the steppe-based empires were established based on the skills to deal with horses and the ability to shoot arrows while riding horses, along with the use of iron ware in the 8th century BC. The steppe-based empires became the foundation for an oceanic empire, which could have been established by the use of warships and warship guns since the 15th Century. Based on those facts, we know that Chosun, Puyo(夫餘), and Koguryo are the products of a developmental process of pastoral nomadic empires on the steppes. Maybe we can easily find the pastoral nomadic identity of the Koguryo more than we expected when we trace the origins and history of the Korean tribe living in the pastures located in the northwest area of Mt. Jangbaek by focusing on pastoral nomadic mobility and organization just as we have investigated the historic origins of Anglo-Saxons in America by focusing on the times before the 15th Century. In the process, we should keep in mind that English culture originated from the Industrial Revolution and was directly delivered to the American continent, although America was far from England and was not an intermediate point on long sojourns either. Further, American culture came back to England in a more advanced form later. The most important thing currently to be resolved is to cause Koreans to look back on their own history in a freer way of thinking and with diverse, profound, and sharp insight, taking away the old and existing conventional recognition that is entangled with complicated interests with Korean people and other countries. The meanings of Chosun, Khori, and Solongos have been interpreted arbitrarily without any historic evidence by the scholars who followed conventional tradition of fixed-minded aristocrats in an agricultural society. If the Siberian cultural properties of the stone age, the earthenware age, the bronze age, and the iron age are analyzed in such a way, archaeological discovery will never be able to contribute to the restoration of the Koguryo's pastoral nomadic identity. One should transcend the errors that tend to interpret the cultural properties discovered in the pastoral nomadic regions as not being differentiated from those of agricultural regions and just interpret them altogether from the agricultural point of view. A more careful intention is required in the interpretation of cultural properties of ancient Korean empires that seem to have been formed due to mutual interactions of pastoral nomadic and agricultural cultures. Also, it is required that the conventional recognition chain of 'reverse-genes' be severed, which has placed more weight on agricultural properties than pastoral nomadic ones, since their settlement on agricultural farms was made after the establishment of their ancient pastoral nomadic empires. There is no reason at all to place priority on stoneware, earthenware, bronze ware, and iron ware than on wooden ware(木器) and other ware which were made of animal skins(皮器), bones and horns(骨角器), in analyzing the history in the regions of reindeer or sheep pastures. Reading ancient Korean history from the perspective of pastoral nomadic history, one feels strongly the instinctive emotions to return to the natural 'mother place'. The reindeer-herding pastoral nomadic identity of the Koguryo people that has been accumulated in volumes in their genes and hidden deep inside and have interacted organically could be reborn with Burqanism(Burqan refers to 不咸 in Chinese), which was their religion by birth and symbolized as the red willow(紅柳=不咸). The mother place of the Koguryo's people is the endless vast green pastures of North Eurasia and North America, where we anticipated the development of Korean livestock farming following the inherent properties in the genes of the reindeer-herding pastoral nomads with Korean ancestors. We anticipate that the place would be the core resource that could contribute to the development of life of living creatures following the inherent properties of their genes and biotechnological factors. In other words, biotechnology used for a search for clues on the well-being of humans could be the fruit brought by Burqanism of the Koguryo people and the fruit of the globalization of Korean livestock farming. It is the Chosun farmer in China come from the vast nomadic reindeer pastures of North Eurasia that resolved the food problem of a billion Chinese people with lowland paddy rice seeds (水稻) by transforming Heilongjiang Province(黑龍江省) into an oceanic lowland paddy rice field(水田). Even Mao Tse-tung(毛擇東) could not resolve the food problem by his revolution campaigns for tens of years. Today is the very time that requires the development of special livestock farming following the inherent properties of the ancient Korean reindeer-herding pastoral nomads that respected the dignity of life on the cold and dry plateau of North Eurasia and the America continent. I suggest that research should be started from the pastures of the Dariganga Steppe in East Mongolia that was the homeland of Hanwoo(韓牛) and the central horse-herding steppe place(牧馬場) of Chingis Khan's Mongolia. The Dariganga Steppe is awash with an affluent natural environment for pastoral nomadic living however, the quality of life of the pastoral nomads there is still low. I suggest we Koreans, the descendents of the Koguryo, should take our first steps for our livestock farming business project and develop the Northern nomadic pastures, here at the pastures of the Dariganga Steppe, which is the Mongolian core place of state-of-the-art technology for military weapons.