• Journal of Venture Innovation
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• v.1 no.1
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• pp.1-14
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• 2018

The 4th Industrial Revolution is making a big change for our company like the tsunami. The CPS system, which is represented by the digital age, is based on the data accumulated in the physical domain and is making business that was not imagined in the past through digital technology. As a result, the business model of the 4th Industrial Revolution era is different from the previous one. In this study, we analyze the trends and the issues of business innovation theory research. Then, the business innovation model of the digital age was compared with the previous period. Based on this, we have searched for a business model suitable for the 4th Industrial Revolution era. The existing business models have many difficulties to explain the model of the digital era. Even though more empirical research should be supported, Michael Porter's diamond model is most suitable for four cases of business models by applying them. Type A sharing outcome with customer is a model that pay differently according to the basis of customer performance. Type B Value Chain Digitalization model provides products and services to customers with faster and lower cost by digitalizing products, services and SCM. Type C Digital Platform is the model that brings the biggest ripple effect. It is a model that can secure profitability by creating new market by creating the sharing economy based on digital platform. Finally, Type D Sharing Resources is a model for building a competitive advantage model by collaborating with partners in related industries. This is the most effective way to complement each other's core competencies and their core competencies. Even though numerous Unicorn companies have differentiated digital competitiveness with many digital technologies in their respective industries in the 4th Industrial Revolution era, there is a limit to the number of pieces to be listed. In future research, it is necessary to identify the business model of the digital age through more specific empirical analysis. In addition, since digital business models may be different in each industry, it is also necessary to conduct comparative analysis between industries

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.570-578
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• 2019

In this era of big data, a variety of government organizations are trying to create new added value via Information Integration. Therefore, several projects related to government agencies' information sharing have activated system connection/integration. The risk factors of system operation, however, have increased as the volume of Information Integration System grows. The interference in information sharing is predicted to affect the operation of the agencies, and the issue will grow even worse with massive impact on civil society when the agency operation is interrupted due to system failures in terms of infrastructure, software, data quality, and security. Diverse studies related to the maintenance of Information System have been conducted, but there is currently no evaluation framework for the operational system of Information Integration between various government agencies. In this respect, this study distinguishes each of the Information System components, Data, IT, People, Process, systematizes with Plan-Do-See, and finally presents a maturity model for Information Integration. Nine derived processes were analyzed through interview and questionnaires from Information Integration System officials, further suggesting maturity stage applying CMMI. This model allows diagnosis of the maturity level of an Information Integration System, and is expected to be utilized as resource for improving organizational processes.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.4
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• pp.101-118
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• 2023

During the COVID-19 pandemic, the utilization rate of neighborhood parks and green spaces increased significantly, and the outbreak served as an opportunity to highlight the values and functions of neighborhood parks and green spaces for urban residents. This study aims to empirically analyze how citizens' movement and the use of neighborhood parks and green spaces changed before and after COVID-19 and examine the social and spatial characteristics that affected these changes. As a research method, first, people's mobility around neighborhood parks and green spaces before and after the COVID-19 pandemic were compared using signal data from telecommunication carriers. Through the analysis of changes in residence time and movement volume, the movement characteristics of citizens after COVID-19 and changes in walking-based park visits were examined. Second, the factors affecting the mobility change in neighborhood parks and green spaces were analyzed. The social and spatial characteristics that affect citizens' visits to neighborhood parks and green spaces before and after COVID-19 were examined through correlation and multiple regression analysis. Subsequently, through cluster analysis, the types of living areas for the post-COVID era were classified from the perspective of the supply and management of neighborhood parks and green spaces services, and directions for improving neighborhood parks and green spaces by type were presented. Major research findings are as follows: First, since the outbreak of COVID-19, activities within 500m of the residence have increased. The amount of stay and walking movement increased in both 2020 and 2021, which means that the need to review the quantitative standards and attractions of neighborhood parks and green spaces has increased considering the changed scope of the walking and living area. Second, the overall number of visits to neighborhood parks and green spaces by walking has increased since the outbreak of COVID-19. The number of visits to neighborhood parks and green spaces centered on the house and the workplace increased significantly. The park green policy in the post-COVID era should be promoted by discovering underprivileged areas, focusing on areas where residential, commercial, and business facilities are concentrated, and improving neighborhood parks and green services in quantitative and qualitative terms. Third, it was found that the higher the level of park green service, the higher the amount of walking movement. It is necessary to use indicators that contribute to improving citizens' actual park green services, such as walking accessibility, rather than looking at the criteria for securing green areas. Fourth, as a result of cluster analysis, five types of neighborhood parks and green spaces were derived in response to the post-COVID era. This suggests that it is necessary to consider the socioeconomic status and characteristics of living areas and the level of park green services required in future park green policies. This study has academic and policy significance in that it has laid the basis for establishing neighborhood parks and green spaces policy in response to the post-COVID era by using various analysis methodologies such as carrier signal data analysis, GIS analysis, and statistical analysis.

• Journal of Korean Society of Transportation
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• v.36 no.3
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• pp.203-215
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• 2018

This study suggests a method to construct large scale dynamic O/D reflecting the characteristic that the passengers' travel patterns change according to the land use patterns of the destination. There are limitations in the existing research about dynamic O/D estimation method, such as the difficulty of collecting data, which can be applied only to a small area, or limiting to a specific transportation network such as highway networks or public transportation networks. In this paper, we propose a method to estimate dynamic O/D without limitation of analysis area based on transportation resources that can be easily collected and used according to the big data era. Clustering analysis was used to calculate the departure time trip distribution ratio based on arrival time and departure time trip distribution function was estimated by each cluster. As a result of the comparison test with the survey data, the estimated distribution function was statistically significant.

• Spatial Information Research
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• v.20 no.1
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• pp.47-57
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• 2012

An Administrative Boundary is the basic of spatial information to cover geographical and regional area. Its importance has arisen in our society at the Smart world era. However, it is difficult to serve exact boundary's lines as administrative boundaries are based on the cadastre lines of land register ; these partly are overlay each other or has gaps. So, it Should be adjusted. But, the maintenance work of administration boundaries causes a conflict or confusion unless we offer concrete procedures and detailed plans previously. Therefore, a rational method is required to prevent side-effects such as confusion, disagreem ent and a conflict etc. In this Study, we present a method and 5 step procedures to make better use in a practical maintenance work. we researched on basic studies of Administrative boundary's concept, history. And we performed a field survey as well as analysis of current problems. considering these results, we suggest usage of various spatial data sources, stake-holders' participation, a method of Nearest district's boundaries to maintain administrative boundaries. Throughout the method, we expect it to serve correct boundary-data to various fields without a big confusion. it is also useful to apply its results not only for re-surveying our land but for recording appropriate boundary-data as rational lines.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.1
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• pp.54-69
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• 2021

In 2020, civilized society's overall lifestyle showed a distinct change from consumable analog media, such as paper, to digital media with the increased penetration of cloud computing, and from wired media to wireless media. Based on these social changes, this work examines whether the use of computer media in the field of landscape architecture is appropriately applied. This study will give directions for new computer media classes in landscape architectural education in the 4th Industrial Revolution era. Landscape architecture is a field that directly proposes the realization of a positive lifestyle and the creation of a living environment and is closely connected with social change. However, there is no clear evidence that landscape architectural education is making any visible change, while the digital infrastructure of the 4th Industrial Revolution, such as Artificial Intelligence (AI), Big Data, autonomous vehicles, cloud networks, and the Internet of Things, is changing the contemporary society in terms of technology, culture, and economy among other aspects. Therefore, it is necessary to review the current state of the use of computer technology and media in landscape architectural education, and also to examine the alternative direction of the curriculum for the new digital era. First, the basis for discussion was made by studying the trends of computational design in modern landscape architecture. Next, the changes and current status of computer media classes in domestic and overseas landscape education were analyzed based on prior research and curriculum. As a result, the number and the types of computer media classes increased significantly between the study in 1994 and the current situation in 2020 in the foreign landscape department, whereas there were no obvious changes in the domestic landscape department. This shows that the domestic landscape education is passively coping with the changes in the digital era. Lastly, based on the discussions, this study examined alternatives to the new curriculum that landscape architecture department should pursue in a new degital world.

• The Journal of Society for e-Business Studies
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• v.19 no.3
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• pp.107-123
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• 2014

Current text mining techniques suffer from the problem that the conventional text representation models cannot express the semantic or conceptual information for the textual documents written with natural languages. The conventional text models represent the textual documents as bag of words, which include vector space model, Boolean model, statistical model, and tensor space model. These models express documents only with the term literals for indexing and the frequency-based weights for their corresponding terms; that is, they ignore semantical information, sequential order information, and structural information of terms. Most of the text mining techniques have been developed assuming that the given documents are represented as 'bag-of-words' based text models. However, currently, confronting the big data era, a new paradigm of text representation model is required which can analyse huge amounts of textual documents more precisely. Our text model regards the 'concept' as an independent space equated with the 'term' and 'document' spaces used in the vector space model, and it expresses the relatedness among the three spaces. To develop the concept space, we use Wikipedia data, each of which defines a single concept. Consequently, a document collection is represented as a 3-order tensor with semantic information, and then the proposed model is called text cuboid model in our paper. Through experiments using the popular 20NewsGroup document corpus, we prove the superiority of the proposed text model in terms of document clustering and concept clustering.

• Journal of KIISE
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• v.44 no.2
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• pp.139-147
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• 2017

Most of the information prevailing in the Internet space consists of textual information. So one of the main topics regarding the huge document analyses that are required in the "big data" era is the development of an automated understanding system for textual data; accordingly, the automation of the keyword extraction for text summarization and abstraction is a typical research problem. But the simple listing of a few keywords is insufficient to reveal the complex semantic structures of the general texts. In this paper, a text-visualization method that constructs a graph by computing the related degrees from the selected keywords of the target text is developed; therefore, two construction models that provide the edge relation are proposed for the computing of the relation degree among keywords, as follows: influence-interval model and word- distance model. The finally visualized graph from the keyword-derived edge relation is more flexible and useful for the display of the meaning structure of the target text; furthermore, this abstract graph enables a fast and easy understanding of the target text. The authors' experiment showed that the proposed abstract-graph model is superior to the keyword list for the attainment of a semantic and comparitive understanding of text.

• The Journal of the Korea Contents Association
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• v.20 no.5
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• pp.448-456
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• 2020

In the era of the Fourth Industrial Revolution, how well the explosive information and data are handled and used is recognized as a problem directly related to the competitiveness of the industry. In particular, the introduction of artificial intelligence technology in the medical field can be said to have a great social impact on its use, and this research was conducted to understand the trends of artificial intelligence according to the range of use case. In this study, the application of artificial intelligence in the healthcare field is divided into four scopes, (1) hospital solutions, (2) personal health care, (3) insurance, and (4) new drug development. Based on various cases and trends in artificial intelligence technology, this study tried to give directions on how to develop artificial intelligence in Korea. In this study, we wanted to find out the use cases of artificial intelligence in various areas of healthcare industry and describe the latest issues in healthcare to help the overall medical industry. The development of artificial intelligence-based medical systems has made it easier to manage the chronic patients, increased the accuracy of cancer or disease diagnosis, and helped developing new drugs faster and more efficiently. Through this study, the medical industry we wanted to give a direction to the future development of artificial intelligence in Korea.