• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.87-107
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• 2016

Understanding market convergence has became essential for small and mid-size enterprises. Identifying convergence items among heterogeneous markets could lead to product innovation and successful market introduction. Previous researches have two limitations. First, traditional researches focusing on patent databases are suitable for detecting technology convergence, however, they have failed to recognize market demands. Second, most researches concentrate on identifying the relationship between existing products or technology. This study presents a platform to identify the opportunity of market convergence by using product databases from a global B2B marketplace. We also attempt to identify convergence opportunity in different industries by applying Structural Hole theory. This paper shows the mechanisms for market convergence: attributes extraction of products and services using text mining and association analysis among attributes, and network analysis based on structural hole. In order to discover market demand, we analyzed 240,002 e-catalog from January 2013 to July 2016.

• Convergence Security Journal
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• v.15 no.4
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• pp.11-18
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• 2015

The new environments arrive such as ICT convergence, cloud computing, and big data, etc., how to take advanta ge of the existing software engineering technologies has become an important key. In addition, the importance of re quirement analysis for secure software and design phase has been shown in the IoT environment While the existing studies have focused on the utilization of the technique applied to IoT environment, the studies for enhancing analys is and design, the prerequisite steps for safely appling these techniques to the site, have been insufficient. So, we tr y to organize research trends based on software engineering and analyze their relationship in this paper. In detail, w e classify the research trends of software engineering to perform research trends meta-analysis, and analyze an ann ual development by years. The flow of the major research is identified by analyzing the correlation of the key word s. We propose the strategies for enhancing the utilization of software engineering techniques to develop high-quality software in the IoT environment.

• Journal of the Korean Society for Library and Information Science
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• v.40 no.2
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• pp.103-123
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• 2006

Assuming that the knowledge creation and retrieval functions could be the most important factors for a successful knowledge management(KM) especially during the promotion stage of KM, this study suggests an e-learning application as one of best methods for producing knowledge and also the integrated knowledge repository model in which learning, knowledge. and information objects can be semantically associated through topic map-based knowledge map. The traditional KM system provides a simple directory-based knowledge map. which can not provide the semantic links between topics or objects. The proposed model can be utilized as a solution to solve the above-mentioned disadvantages of the traditional models. In order to collect the basic data for the proposed model, first, case studies utilizing interviews and surveys were conducted targeting at three Korean insurance companies' knowledge managers(or e-learning managers) and librarians. Second, the related studies and other topic map-based pilot systems were investigated.

• Spatial Information Research
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• v.23 no.5
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• pp.1-7
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• 2015

Twitter has the fast propagation and diffusion of information compare to other SNS. Therefore, many researches about detecting real-time event using twitter are progressing. Twitter real-time event detecting system assumes every twitter user as a sensor and analyzes their written tweet in order to detect the event. Researches that are related to this twitter have already obtained good results but confronted the limits because of some problems. Especially, many existing researches are using the method that can trace an event location by using GPS coordinate. However, it can be suggested a definite limitation through the present user's skeptical responses about making personal location information public. Therefore, this paper suggests the method that traces the location information in tweet contents text without using the provided location information from twitter. Associated words were grouped by using the keyword that extracted in tweet contents text. The place that the events have occurred and whether the events have surely occurred are detected by this experiment using this algorithm. Furthermore, this experiment demonstrated the necessity of the suggested methods by showing faster detection compare to the other existing media.

• The Journal of the Korea Contents Association
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• v.8 no.11
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• pp.412-425
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• 2008

One of the important elements for improving academic achievement of learners in education through e-learning is to support learners to study by finding questions they want with providing various evaluation questions. However, most of question retrieval systems usually depend on keyword search based on only a syntactical analysis and/or a hierarchical browsing system classified by the topics of subjects. In such a system it is not easy to find integrative questions associated with each other. In order to improve this problem, in this paper we proposed a question management and retrieval system which allows users to easily manage questions and also to effectively find questions for study on the Web. Then, we implemented a system that gives to access questions for the domain of C language programming. The system makes it possible to easily search questions related to not only a single theme but also questions integrated by interrelationship between topics and questions. This is done by supporting to be able to retrieve questions according to conceptual interrelationships between questions from user query. Consequently, it is expected that the proposed system will provide learners to understand the basic theories and the concepts of the subjects as well as to improve the ability of comprehensive knowledge utilization and problem-solving.

• Journal of Internet Computing and Services
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• v.16 no.3
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• pp.105-116
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• 2015

The Sewol ferry catastrophe that took place on April 16 2014 was unprecedented in terms of its sociopolitical implications, which had reverberated throughout the Korean nation. Mindful of such distinct characteristics of the Sewol ferry catastrophe, this thesis looks into the salience of the agendas portrayed in Twitter and Portal News coverage on the disaster and the correlation between the attribute-specific agendas of the foregoing mediums by making use of the agenda rank order correlation method. Extraction and analysis of big data revealed that first, while the hypothesis that there were little difference in terms of salience among the main agendas between Twitter and Portal News was dismissed, the rank order correlation proved to be high as regards the main agendas on Twitter and Portal News. This signifies that Twitter agendas exert influence over those on Portal News. Next, and regarding the five main agendas on the incident, there existed differences in salience between the attribute-specific agendas of the two mediums, with low figures for corresponding rank order correlations. Such results signify that Twitter and Portal News have little influence over each other as regards their agenda rank order correlation.

• Journal of Digital Convergence
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• v.16 no.7
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• pp.27-34
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• 2018

This study was conducted to identify the contents of major consulting needs of SMEs using Big Data and to suggest the efficiency of operation. The subjects of the study were counseling cases posted on the website of the Business Support Center of the Ministry of SMEs and Startups. To do this, from 2009 to March 2018, we crawled about 7,000 cases of counseling cases, followed by word cloud analysis centering on effective keyword. The main results were as follows: First, the frequency of counseling cases in each field was found in the order of establishment, management strategy, human resources, financial order. Second, in word cloud analysis, the most frequent keyword related to counseling demand were small businesses, exports, methods, procedures, registration and authentication. In this study, we obtained research results that we can improve the efficiency of the policy in real time from a new point of view by conducting big data analysis on public policy.

• Land and Housing Review
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• v.13 no.2
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• pp.81-90
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• 2022

This study is based on social needs for green healing spaces assumed to enhance mental health in a city. This study proposes development directions through the analysis of modern social recognition factors for neighborhood gardens. As a research method, web information data was collected using Textom among big data tools. Text Mining was conducted to extract elements and analyze their relationship through keyword analysis, network analysis, and cluster analysis. As a result, first, the healing space and the healing environment were creating an eco-friendly healthy environment in a space close to the neighborhood within the city. Second, neighborhood gardens included projects and activities that involved government, local administration, and citizens by linking facilities as well as living culture and urban environments. These gardens have been reinforced through green welfare and service programs. In conclusion, friendly gardens in the neighborhood for the purpose of public interest, which are beneficial to mental health, are green infrastructures as a healing environment that can produce positive effects.

• Proceedings of the Korea Database Society Conference
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• 2008.05a
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• pp.389-403
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• 2008

As the information of web dramatically increase, the existing web reveals more and more limitations in information search because web pages are designed only for human consumption by mixing content with presentation. In order to improve this situation, the Semantic Web comes on the stage by W3C. Semantic web is based on ontology that defines relationships between resources and it is enough to bring a significant advancement in web search. But to do this, the Semantic Web must provide a novel search and visualization methods which can make users instantly and intuitively understand why and how the results are retrieved because ontology has formal explicit descriptions of meaning. In this paper, we propose a semantic association-based search methodology that consists of how to find relevant information for a given user's query in the ontology, that is, a semantic network of resources and properties and how to provide proper visualization and navigation methods on the results. From this work, users can search the semantically associated resources for their query and also navigate such associations between resources.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.19-33
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• 2013

In this research, a proposed Dynamic Virtual Ontology using Tags (DyVOT) supports dynamic search of resources depending on user's requirements using tags from social web driven resources. It is general that the tags are defined by annotations of a series of described words by social users who usually tags social information resources such as web-page, images, u-tube, videos, etc. Therefore, tags are characterized and mirrored by information resources. Therefore, it is possible for tags as meta-data to match into some resources. Consequently, we can extract semantic relationships between tags owing to the dependency of relationships between tags as representatives of resources. However, to do this, there is limitation because there are allophonic synonym and homonym among tags that are usually marked by a series of words. Thus, research related to folksonomies using tags have been applied to classification of words by semantic-based allophonic synonym. In addition, some research are focusing on clustering and/or classification of resources by semantic-based relationships among tags. In spite of, there also is limitation of these research because these are focusing on semantic-based hyper/hypo relationships or clustering among tags without consideration of conceptual associative relationships between classified or clustered groups. It makes difficulty to effective searching resources depending on user requirements. In this research, the proposed DyVOT uses tags and constructs ontologyfor effective search. We assumed that tags are extracted from user requirements, which are used to construct multi sub-ontology as combinations of tags that are composed of a part of the tags or all. In addition, the proposed DyVOT constructs ontology which is based on hierarchical and associative relationships among tags for effective search of a solution. The ontology is composed of static- and dynamic-ontology. The static-ontology defines semantic-based hierarchical hyper/hypo relationships among tags as in (http://semanticcloud.sandra-siegel.de/) with a tree structure. From the static-ontology, the DyVOT extracts multi sub-ontology using multi sub-tag which are constructed by parts of tags. Finally, sub-ontology are constructed by hierarchy paths which contain the sub-tag. To create dynamic-ontology by the proposed DyVOT, it is necessary to define associative relationships among multi sub-ontology that are extracted from hierarchical relationships of static-ontology. The associative relationship is defined by shared resources between tags which are linked by multi sub-ontology. The association is measured by the degree of shared resources that are allocated into the tags of sub-ontology. If the value of association is larger than threshold value, then associative relationship among tags is newly created. The associative relationships are used to merge and construct new hierarchy the multi sub-ontology. To construct dynamic-ontology, it is essential to defined new class which is linked by two more sub-ontology, which is generated by merged tags which are highly associative by proving using shared resources. Thereby, the class is applied to generate new hierarchy with extracted multi sub-ontology to create a dynamic-ontology. The new class is settle down on the ontology. So, the newly created class needs to be belong to the dynamic-ontology. So, the class used to new hyper/hypo hierarchy relationship between the class and tags which are linked to multi sub-ontology. At last, DyVOT is developed by newly defined associative relationships which are extracted from hierarchical relationships among tags. Resources are matched into the DyVOT which narrows down search boundary and shrinks the search paths. Finally, we can create the DyVOT using the newly defined associative relationships. While static data catalog (Dean and Ghemawat, 2004; 2008) statically searches resources depending on user requirements, the proposed DyVOT dynamically searches resources using multi sub-ontology by parallel processing. In this light, the DyVOT supports improvement of correctness and agility of search and decreasing of search effort by reduction of search path.