• Journal of Life Science
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• v.31 no.10
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• pp.913-921
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• 2021

Limb-girdle muscular dystrophy (LGMD) which is characterized by progressive muscle weakening of the hip and shoulder shows both dominant and recessive inheritances with many pathogenic genes including TTN. This study performed to identify genetic causes of a male patient with late onset (45 years old) autosomal recessive LGMD and atrial flutter. By application of the whole exome sequencing, we identified bi-allelic variants of TTN gene in the patient. One allele had a single missense variant of [c.24124G>T (p.V8042F)], while the other allele consisted of three missense variants of [c.29222G>C (p.R9741P) + c.67490A>G (p.H22497R) + c.75376C>T (p.R25126C)]. The p.V8042F allele was transmitted from his mother, while the other haplotype allele was putatively transmitted from his father. His two unaffected sons had only the p.R9741P. These variants have been not reported or rarely reported in the public human genome databases (1,000 Genome, gnomAD, and KRGDB). Most variants were located in the highly conserved immunoglobulin or fibronectin domains and were predicted to be pathogenic by the in silico analyses. The TTN giant protein plays a key role in muscle assembly, force transmission at the Z-line, and maintenance of resting tension in the I-band. In conclusion, we think that these bi-allelic compound heterozygous mutations may play a role as the genetic causes of the LGMD phenotype.

• 기호학연구
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• no.56
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• pp.45-70
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• 2018

Criticism of the postmodern philosophy of the system of representation, which has continued since the Renaissance, is based on a critique of the dichotomy that separates the subjects and objects and the environment from the human being. Interactivity, highlighted in a series of works emerging as postmodern trends in the 1960s, was transmitted to an interactive aspect of digital art in the late 1990s. The key feature of digital art is the possibility of infinite variations reflecting unpredictable changes based on public participation on the spot. In this process, the importance of computer programs is highlighted. Instead of using the existing program as it is, more and more artists are creating and programming their own algorithms or creating unique algorithms through collaborations with programmers. We live in an era of paradigm shift in which programming itself must be considered as a creative act. Simulation technology and VR technology draw attention as a technique to represent the meaning of reality. Simulation technology helps artists create experimental works. In fact, Baudrillard's concept of Simulation defines the other reality that has nothing to do with our reality, rather than a reality that is extremely representative of our reality. His book Simulacra and Simulation refers to the existence of a reality entirely different from the traditional concept of reality. His argument does not concern the problems of right and wrong. There is no metaphysical meaning. Applying the concept of simulation to algorithmic art, the artist models the complex attributes of reality in the digital system. And it aims to build and integrate internal laws that structure and activate the world (specific or individual), that is to say, simulate the world. If the images of the traditional order correspond to the reproduction of the real world, the synthesized images of algorithmic art and simulated space-time are the forms of art that facilitate the experience. The moment of seeing and listening to the work of Ian Cheng presented in this article is a moment of personal experience and the perception is made at that time. It is not a complete and closed process, but a continuous and changing process. It is this active and situational awareness that is required to the audience for the comprehension of post-representation's forms.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.215-235
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• 2024

As artificial intelligence (AI) technology is recognized as a key technology that will determine future national competitiveness, competition for AI technology and industry promotion policies in major countries is intensifying. This study aims to present implications for domestic policy making by analyzing the policies of major countries on the start-up of AI companies, which are the basis of the AI industry ecosystem. The top four countries and the EU for the number of new investment attraction companies in the 2023 AI Index announced by the HAI Research Institute at Stanford University in the United States were selected, The United States enacted the National AI Initiative Act (NAIIA) in 2021. Through this law, The US Government is promoting continued leadership in the United States in AI R&D, developing reliable AI systems in the public and private sectors, building an AI system ecosystem across society, and strengthening DB management and access to AI policies conducted by all federal agencies. In the 14th Five-Year (2021-2025) Plan and 2035 Long-term Goals held in 2021, China has specified AI as the first of the seven strategic high-tech technologies, and is developing policies aimed at becoming the No. 1 AI global powerhouse by 2030. The UK is investing in innovative R&D companies through the 'Future Fund Breakthrough' in 2021, and is expanding related investments by preparing national strategies to leap forward as AI leaders, such as the implementation plan of the national AI strategy in 2022. Israel is supporting technology investment in start-up companies centered on the Innovation Agency, and the Innovation Agency is leading mid- to long-term investments of 2 to 15 years and regulatory reforms for new technologies. The EU is strengthening its digital innovation hub network and creating the InvestEU (European Strategic Investment Fund) and AI investment fund to support the use of AI by SMEs. This study aims to contribute to analyzing the policies of major foreign countries in making AI company start-up policies and providing a basis for Korea's strategy search. The limitations of the study are the limitations of the countries to be analyzed and the failure to attempt comparative analysis of the policy environments of the countries under the same conditions.

• Journal of Life Science
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• v.34 no.1
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• pp.48-58
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• 2024

Salmonella is a common food-borne intracellular bacterial pathogen that has triggered significant public health concerns. Salmonella hosts' genetic factors play a pivotal role in determining their susceptibility to the pathogen. Cysteine-rich intestinal protein 1 (CRIP1), a member of LIM/double zinc finger protein family, is widely expressed in humans, such as in the lungs, spleen, and especially the gut. Recently, CRIP1 has been reported as a key marker of several immune disorders; however, the effect of CRIP1 on bacterial infection remains unknown. We aimed to elucidate the relationship between Salmonella infection and CRIP1 gene deficiency, as Salmonella spp. is known to invade the Peyer's patches of the small intestine, where CRIP1 is highly expressed. We found that CRIP1-deficient conditions could not alter the characteristics of bone marrow-derived myeloid cells in terms of phagocytosis on macrophages and the activation of costimulatory molecules on dendritic cells using ex vivo differentiation. Moreover, flow cytometry data showed comparable levels of MHCII⁺CD11b⁺CD11c⁺ dendritic cells and MHCII⁺F4/80⁺CD11b⁺ macrophages between WT and CRIP1 knockout (KO) mice. Interestingly, the basal population of monocytes in the spleen and neutrophils in MLNs is more abundant in a steady state of CRIP1 KO mice than WT mice. Here, we demonstrated that the CRIP1 genetic factor plays dispensable roles in host susceptibility to Salmonella Typhimurium infections and the activation of myeloid cells. In addition, differential immune cell populations without antigen exposure in CRIP1 KO mice suggest that the regulation of CRIP1 expression may be a novel immunotherapeutic approach to various infectious diseases.

• Journal of Global Scholars of Marketing Science
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• v.19 no.3
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• pp.5-16
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• 2009

Corporate social responsibility (CSR) activities have been shown to be potential factors that can improve corporate image and increase the ability of corporations to compete. However, most previous studies related to CSR activities investigated how these activities influence product and corporate evaluation, as well as corporate image. In addition, some researchers treated consumers' perceptions of corporate motives as moderator variables in evaluating the relationship between corporate social responsibilities and consumer response. However, motive-based theories have some weaknesses. Corporate social responsibility activities cause two motives(egoistic vs. altruistic) for consumers, but recently, Vlachos et al. (2008) argued that these motives should be segmented. Thus, it is possible to transform the original theory into a modified theory model (persuasion knowledge model, PKM). Vlachos et al. (2008) segmented corporate social responsibility motives into four types and compared the effects of these motives on customer loyalty. Prior studies have proved that CSR activities with positive motives have positive influences on customer loyalty. However, the psychological reasons underlying this finding have not been determined empirically. Thus, the objectives of this research are twofold. First, we attempt to determine why most customers favor companies that they feel have positive motives for their corporate social responsibility activities. Second, we attempt to measure the effects of consumers' reciprocity when society benefits from corporate social responsibility activities. The following research hypotheses are constructed. H1: Values-driven motives for corporate social responsibility activities have a positive influence on the perceived reciprocity. H2: Stakeholder-driven motives for corporate social responsibility activities have a negative influence on the perceived reciprocity. H3: Egoistic-driven motives for corporate social responsibility activities have a negative influence on perceived reciprocity. H4: Strategic-driven motives for corporate social responsibility activities have a negative influence on perceived reciprocity. H5: Perceived reciprocity for corporate social responsibility activities has a positive influence on consumer loyalty. A single company is selected as a research subject to understand how the motives behind corporate social responsibility influence consumers' perceived reciprocity and customer loyalty. A total sample of 200 respondents was selected for a pilot test. In addition, to ensure a consistent response, we ensured that the respondents were older than 20 years of age. The surveys of 172 respondents (males-82, females-90) were analyzed after 28 invalid questionnaires were excluded. Based on our cutoff criteria, the model fit the data reasonably well. Values-driven motives for corporate social responsibility activities had a positive effect on perceived reciprocity (t = 6.75, p < .001), supporting H1. Morales (2005) also found that consumers appreciate a company's social responsibility efforts and the benefits provided by these efforts to society. Stakeholder-driven motives for corporate social responsibility activities did not affect perceived reciprocity (t = -.049, p > .05). Thus, H2 was rejected. Egoistic-driven motives (t = .3.11, p < .05) and strategic-driven (t = -4.65, p < .05) motives had a negative influence on perceived reciprocity, supporting H3 and H4, respectively. Furthermore, perceived reciprocity had a positive influence on consumer loyalty (t = 4.24, p < .05), supporting H5. Thus, compared with the general public, undergraduate students appear to be more influenced by egoistic-driven motives. We draw the following conclusions from our research findings. First, value-driven attributions have a positive influence on perceived reciprocity. However, stakeholder-driven attributions have no significant effects on perceived reciprocity. Moreover, both egoistic-driven attributions and strategic-driven attributions have a negative influence on perceived reciprocity. Second, when corporate social responsibility activities align with consumers' reciprocity, the efforts directed towards social responsibility activities have a positive influence on customer loyalty. In this study, we examine whether the type of motivation affects consumer responses to CSR, and in particular, we evaluate how CSR motives can influence a key internal factor (perceived reciprocity) and behavioral consumer outcome (customer loyalty). We demonstrate that perceived reciprocity plays a mediating role in the relationship between CSR motivation and customer loyalty. Our study extends the research on consumer CSR-inferred motivations, positing them as a direct indicator of consumer responses. Furthermore, we convincingly identify perceived reciprocity as a sub-process mediating the effect of CSR attributions on customer loyalty. Future research investigating the ultimate behavior and financial impact of CSR should consider that the impacts of CSR also stem from perceived reciprocity. The results of this study also have important managerial implications. First, the central role that reciprocity plays indicates that managers should routinely measure how much their socially responsible actions create perceived reciprocity. Second, understanding how consumers' perceptions of CSR corporate motives relate to perceived reciprocity and customer loyalty can help managers to monitor and enhance these consumer outcomes through marketing initiatives and management of CSR-induced attribution processes. The results of this study will help corporations to understand the relative importance of the four different motivations types in influencing perceived reciprocity.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.163-176
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• 2014

Social media is becoming the platform for users to communicate their activities, status, emotions, and experiences to other people. In recent years, microblogs, such as Twitter, have gained in popularity because of its ease of use, speed, and reach. Compared to a conventional web blog, a microblog lowers users' efforts and investment for content generation by recommending shorter posts. There has been a lot research into capturing the social phenomena and analyzing the chatter of microblogs. However, measuring television ratings has been given little attention so far. Currently, the most common method to measure TV ratings uses an electronic metering device installed in a small number of sampled households. Microblogs allow users to post short messages, share daily updates, and conveniently keep in touch. In a similar way, microblog users are interacting with each other while watching television or movies, or visiting a new place. In order to measure TV ratings, some features are significant during certain hours of the day, or days of the week, whereas these same features are meaningless during other time periods. Thus, the importance of features can change during the day, and a model capturing the time sensitive relevance is required to estimate TV ratings. Therefore, modeling time-related characteristics of features should be a key when measuring the TV ratings through microblogs. We show that capturing time-dependency of features in measuring TV ratings is vitally necessary for improving their accuracy. To explore the relationship between the content of microblogs and TV ratings, we collected Twitter data using the Get Search component of the Twitter REST API from January 2013 to October 2013. There are about 300 thousand posts in our data set for the experiment. After excluding data such as adverting or promoted tweets, we selected 149 thousand tweets for analysis. The number of tweets reaches its maximum level on the broadcasting day and increases rapidly around the broadcasting time. This result is stems from the characteristics of the public channel, which broadcasts the program at the predetermined time. From our analysis, we find that count-based features such as the number of tweets or retweets have a low correlation with TV ratings. This result implies that a simple tweet rate does not reflect the satisfaction or response to the TV programs. Content-based features extracted from the content of tweets have a relatively high correlation with TV ratings. Further, some emoticons or newly coined words that are not tagged in the morpheme extraction process have a strong relationship with TV ratings. We find that there is a time-dependency in the correlation of features between the before and after broadcasting time. Since the TV program is broadcast at the predetermined time regularly, users post tweets expressing their expectation for the program or disappointment over not being able to watch the program. The highly correlated features before the broadcast are different from the features after broadcasting. This result explains that the relevance of words with TV programs can change according to the time of the tweets. Among the 336 words that fulfill the minimum requirements for candidate features, 145 words have the highest correlation before the broadcasting time, whereas 68 words reach the highest correlation after broadcasting. Interestingly, some words that express the impossibility of watching the program show a high relevance, despite containing a negative meaning. Understanding the time-dependency of features can be helpful in improving the accuracy of TV ratings measurement. This research contributes a basis to estimate the response to or satisfaction with the broadcasted programs using the time dependency of words in Twitter chatter. More research is needed to refine the methodology for predicting or measuring TV ratings.

• (The) Research of the performance art and culture
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• no.42
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• pp.47-75
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• 2021

This thesis looks into the interpretation of the Korean War and mystery genre in Korea in the 1970s by analyzing the special drama , in which the theme was directly related to the Korean War, airing through MBC in 1979. It begins by finding the change in direction in the 1970s when the world of TV was dictated through the heavy censorship and the memory of the war by the government. It also looks at the intentions of the producer who was taking in the new way and the viewers who also accepted this drama and its reflections. In order to gain some insights into these issues, it compares between the drama "The Last Witness" and the original novel by Seong-jong Kim who holds the same time to see the way in which this is dramatized. The drama, "The Last Witness", was produced with a plan to generate a high-quality special drama which combined both artistry and sense of purpose. Nevertheless, as watching TV became a leisurely past-time during this period, TV dramas become more aggressive and suggestive in order to attract viewers. This ultimately was encored with obstacles due to the regime and the heavy censorship at the time. The genre of special drama that is well known in South Korea, is designed as an art form to satisfy both their unique artistry and its purpose. The conflict is seen between the key elements of the artistic drama crated by the producers and the 'encouraged' elements that often are needed to engage the viewers. Thus, more often than not, special dramas defeat the original intention of national harmony, encouraged by the regime. This is due to the 'novelty' aspect which grows from the effort of bringing enjoyment to viewers whilst also trying to achieve the artistic drama to life. Alongside this, crime element in this drama is designed in a way that visually embodies the process of deduction, becoming a new possibility to secure the reality of the times. However, it was also a paradoxical existence since it was indicated as an example of unrefined culture that lost its original intention. In that way, it is worth to think that detective suspense stories, which were not popular in Korea, influenced viewers as a tv drama series in the 1970s through the various elements that compose the genre. They went through a process of transplantation and acceptance whilst also attempting to satisfy the viewers and their encouraged elements to engage them. As is well known, crime drama in Korea has its own style by mixing anticommunism and detective reasoning. This combination is found in the way in which the genre naturally forms through the elements selected and excluded in the dramatization of "The Last Witness". The point is that the special drama "The Last Witness" can be seen as an intermediate form that shows the tendency of transformation from the detective reasoning form alongside the crime aspects as TV dramas began to include anticommunism messaging and investigation in the 1970s. In conclusion, when the detective reasoning is used as an element in a TV drama, it shows the trust of the public system and it constantly seeks the possibility of circumventing the political interpretation. The memories of the war is seen as a tool that neutralizes the dismal imaginations inscribed on the dark side of society and the system. As a result, "The Last Witness", broadcasted at the end of the Yushin regime in Korea, is a strange result which combines the logic of a special drama and the encouraged characteristics of television dramas. The viewers' desire which is the discussion about the hidden traces from the texts needs to be restored again.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.33-54
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• 2021

With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.149-169
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• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.67-88
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• 2018

Until recently, as we recognize the significance of sustainable growth and competitiveness of small-and-medium sized enterprises (SMEs), governmental support for tangible resources such as R&D, manpower, funds, etc. has been mainly provided. However, it is also true that the inefficiency of support systems such as underestimated or redundant support has been raised because there exist conflicting policies in terms of appropriateness, effectiveness and efficiency of business support. From the perspective of the government or a company, we believe that due to limited resources of SMEs technology development and capacity enhancement through collaboration with external sources is the basis for creating competitive advantage for companies, and also emphasize value creation activities for it. This is why value chain network analysis is necessary in order to analyze inter-company deal relationships from a series of value chains and visualize results through establishing knowledge ecosystems at the corporate level. There exist Technology Opportunity Discovery (TOD) system that provides information on relevant products or technology status of companies with patents through retrievals over patent, product, or company name, CRETOP and KISLINE which both allow to view company (financial) information and credit information, but there exists no online system that provides a list of similar (competitive) companies based on the analysis of value chain network or information on potential clients or demanders that can have business deals in future. Therefore, we focus on the "Value Chain Network System (VCNS)", a support partner for planning the corporate business strategy developed and managed by KISTI, and investigate the types of embedded network-based analysis modules, databases (D/Bs) to support them, and how to utilize the system efficiently. Further we explore the function of network visualization in intelligent value chain analysis system which becomes the core information to understand industrial structure ystem and to develop a company's new product development. In order for a company to have the competitive superiority over other companies, it is necessary to identify who are the competitors with patents or products currently being produced, and searching for similar companies or competitors by each type of industry is the key to securing competitiveness in the commercialization of the target company. In addition, transaction information, which becomes business activity between companies, plays an important role in providing information regarding potential customers when both parties enter similar fields together. Identifying a competitor at the enterprise or industry level by using a network map based on such inter-company sales information can be implemented as a core module of value chain analysis. The Value Chain Network System (VCNS) combines the concepts of value chain and industrial structure analysis with corporate information simply collected to date, so that it can grasp not only the market competition situation of individual companies but also the value chain relationship of a specific industry. Especially, it can be useful as an information analysis tool at the corporate level such as identification of industry structure, identification of competitor trends, analysis of competitors, locating suppliers (sellers) and demanders (buyers), industry trends by item, finding promising items, finding new entrants, finding core companies and items by value chain, and recognizing the patents with corresponding companies, etc. In addition, based on the objectivity and reliability of the analysis results from transaction deals information and financial data, it is expected that value chain network system will be utilized for various purposes such as information support for business evaluation, R&D decision support and mid-term or short-term demand forecasting, in particular to more than 15,000 member companies in Korea, employees in R&D service sectors government-funded research institutes and public organizations. In order to strengthen business competitiveness of companies, technology, patent and market information have been provided so far mainly by government agencies and private research-and-development service companies. This service has been presented in frames of patent analysis (mainly for rating, quantitative analysis) or market analysis (for market prediction and demand forecasting based on market reports). However, there was a limitation to solving the lack of information, which is one of the difficulties that firms in Korea often face in the stage of commercialization. In particular, it is much more difficult to obtain information about competitors and potential candidates. In this study, the real-time value chain analysis and visualization service module based on the proposed network map and the data in hands is compared with the expected market share, estimated sales volume, contact information (which implies potential suppliers for raw material / parts, and potential demanders for complete products / modules). In future research, we intend to carry out the in-depth research for further investigating the indices of competitive factors through participation of research subjects and newly developing competitive indices for competitors or substitute items, and to additively promoting with data mining techniques and algorithms for improving the performance of VCNS.