• Journal of Digital Convergence
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• v.17 no.12
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• pp.255-260
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• 2019

Real-time search keywords are not only limited to search keywords that are rapidly increasing interest in real-time, but also have a limitation that they are difficult to determine the sustainability as there is a difference in ranking between portal sites. Estimating sustainability for real-time search keywords is significant in terms of overcoming these limitations and providing some predictability. In particular, long-term search keywords that last for more than a month are of high value as long-lasting social issues. Therefore, in this paper, we analyze the interest based on the ranking of the real-time search keywords and the duration based on sustained weeks, days and hours of real-time search keywords by each portal site and the integrated portal site, and then estimating sustainability based on high level of interest and duration, and present a method to derive real-time search issues with high long-term sustainability.

• Journal of Intelligence and Information Systems
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• v.14 no.1
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• pp.67-84
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• 2008

This paper proposes a design and realization for the semantic search system. The proposed model includes three Architecture Layers of a Semantic Search System ; (they are conceptually named as) the Knowledge Acquisition, the Knowledge Representation and the Knowledge Utilization. Each of these three Layers are designed to interactively work together, so as to maximize the users' information needs. The Knowledge Acquisition Layer includes index and storage of Semantic Metadata from various source of web contents(eg : text, image, multimedia and so on). The Knowledge Representation Layer includes the ontology schema and instance, through the process of semantic search by ontology based query expansion. Finally, the Knowledge Utilization Layer includes the users to search query intuitively, and get its results without the users'knowledge of semantic web language or ontology. So far as the design and the realization of the semantic search site is concerned, the proposedsemantic search system will offer useful implications to the researchers and practitioners so as to improve the research level to the commercial use.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.93-111
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• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.

• The KIPS Transactions:PartB
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• v.18B no.2
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• pp.73-82
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• 2011

There are a lot of people trying to solve the Traveling Salesman Problem (TSP) by using the Meta Heuristic Algorithms. TSP is an NP-Hard problem, and is used in testing search algorithms and optimization algorithms. Also TSP is one of the models of social problems. Many methods are proposed like Hybrid methods and Custom-built methods in Meta Heuristic. In this paper, we propose the S-MINE Algorithm to use the MINE Algorithm introduced in 2009 on the TSP.

• 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.

• STI Policy Review
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• v.7 no.1
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• pp.1-16
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• 2016

Technology supply has been the main thrust of the Korean government's science & technology policy, focusing on the development and acquisition of new technology in line with the catching-up strategy of economic growth and industrial development. However, new social or societal problems have become major government policy issues, heralding new innovation policy aimed to address them. Such new policy initiatives for social problem-solving present a niche where the existing system of government innovation policy process is challenged, including such processes as goal-setting, planning, implementation, project management, and evaluation. The rigidity of the existing institution of government innovation policy, however, still shapes the content and progression of innovation policy for social problem-solving. This study reviews Korean innovation policy for social problem-solving as a policy niche, and aims to clarify its challenges and opportunities. It uses a system transition framework to explain the emergence and evolution of the innovation policy niche in Korea. The main research question is to what extent and in what aspect the existing innovation policy regime shaped innovation policy for social problem-solving. The study examines the inertia of the current paradigm of innovation policies and R&D programs, and sheds light on the search for a distinctive identity for innovation policies that tackles social problems.

• The Journal of Korean Society for School & Community Health Education
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• v.7
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• pp.87-99
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• 2006

Objectives: This study is to draw the design of the program which is improve school health promotion participation by applying the Social Ecological Model based on the literature review on the health promotion. Methods: Literature review was carried out based on 5 factors of social ecological model using computer search engines of Google, ProQuest, and Riss4U. Results; Social Ecological Model is consist of individual, interpersonal, institutional/organizational, community, and policy. Individual sphere is drawn from Health Belief Model, interpersonal sphere is Social Support Theory, institutional/ organizational sphere is institutional resources theory, community sphere is community model, and policy sphere is Social Marketing Theory. The literature review show that the important variables affecting health promotion exist in each sphere. Individual sphere has social economic status, age, sex, sensitivity and specificity of illness, self-efficacy. Interpersonal sphere has support and use of family, friend and neighbor. Institutional/Organizational sphere has environment service reliability and utility. Conclusions: Community sphere has distance, neighborhood safety, interrelationship among institutions. Policy sphere has cost, legislation advertisement, lobby and concern and leadership of Institution.

• Journal of Information Technology Services
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• v.12 no.3
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• pp.73-93
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• 2013

This paper is a research on perceived risk of social commerce which has influence on purchase intention. This study uses a 3-dimension concept of perceived risk and then, deals with influencing a purchase intention. In addition, to search other influential variable to purchase intention, this study adds a variable with expected moderating effect. This study proposes several hypothesis and processes an experiment to test and attest these hypothesis. This study attempts to analyze the effect that perceived risk in social commerce has on purchase intention. By the results of data analysis, all dimensions of perceived risk are found to have significant negative effects on purchase intention. In addition, this study proves moderating roles of valence of WOM Information on purchase intention. Additional managerial implications are also discussed.

• Journal of Information Technology Applications and Management
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• v.20 no.3
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• pp.31-42
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• 2013

With a focus on Facebook, the most popular social network service (SNS), this research tried to find out which type of motivation makes users to spend more time on the SNS and in the same vein, which type of motivation makes users to believe that they will continue to use the SNS. The result shows that the need for play (entertaining) and image as social reputation affect the current amount of use, while the needs for information search, building relationship, and entertainment would make them continue to use.

• The Korean Journal of Emergency Medical Services
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• v.27 no.1
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• pp.59-70
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• 2023

Purpose: This study was designed to identify the effects of social support, learning flow, and learning satisfaction on academic achievement in university students. Methods: This study involved university students who agreed to participate the investigation in D City using a structured online questionnaire from December 1, 2022 to December 31, 2022. Results: Social support, learning flow, learning satisfaction, and academic achievement had significant correlations. The influencing factors of academic achievement were age and learning flow, with an explanatory power of 20%. Conclusion: Further active management and attention are imperative for vulnerable students in high-age groups to search for the ways to improve learning flow.