• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.65-78
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• 2009

The most commonly adopted approach to find valuable information from tree data is to extract frequently occurring subtree patterns from them. Because mining frequent tree patterns has a wide range of applications such as xml mining, web usage mining, bioinformatics, and network multicast routing, many algorithms have been recently proposed to find the patterns. However, existing tree mining algorithms suffer from several serious pitfalls in finding frequent tree patterns from massive tree datasets. Some of the major problems are due to (1) modeling data as hierarchical tree structure, (2) the computationally high cost of the candidate maintenance, (3) the repetitious input dataset scans, and (4) the high memory dependency. These problems stem from that most of these algorithms are based on the well-known apriori algorithm and have used anti-monotone property for candidate generation and frequency counting in their algorithms. To solve the problems, we base a pattern-growth approach rather than the apriori approach, and choose to extract maximal frequent subtree patterns instead of frequent subtree patterns. The proposed method not only gets rid of the process for infrequent subtrees pruning, but also totally eliminates the problem of generating candidate subtrees. Hence, it significantly improves the whole mining process.

• Journal of the Economic Geographical Society of Korea
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• v.15 no.1
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• pp.26-41
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• 2012

This study investigates the time-space characteristics of intra-urban passenger flows in the Metropolitan Seoul area. In particular, we analyze the relationships between transit ridership and land use through the use of the subway passenger flow data obtained from the transit transaction databases. For this purpose, the strength of each subway station, i.e., the number of total in-coming and out-going passengers at each station, in the morning, afternoon, and evening, is calculated and visualized, which reflects urban land use patterns. Then the subway stations are classified into four groups via a hierarchical analysis of the in-coming and out-going passenger flows at 353 stations. Each group appears to have characteristic properties according to the region, e.g., residential areas and central business districts. This has been confirmed by the analysis which probes explicitly the relationship between the local socio-economic variables and station groups. This analysis, disclosing the inter-relationship between the subway network and urban land use, may be useful at various stages in urban as well as transportation planning, and provides analytical tools for a wide spectrum of applications ranging from impact evaluation to decision-making and planning support.

• Korean journal of communication and information
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• v.22
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• pp.167-210
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• 2003

This study examined the organizational status of female employees in three major over-the-air broadcasting companies in South Korea in terms of both formal and informal organizational structures. Quantitative and qualitative analyses were conducted for the data collected through the official statistical documents on Korean broadcasting industries, in-person/telephone interviews, and survey. The results show that the proportion of female employees in the three major over-the-air broadcasting companies remained relatively unchanged at about as low as 10 per cents during the past 10 years, Furthermore, most of them are positioned in the lower hierarchical positions. The informal organizational status of female employees in terms of informal organizational culture, informal network, and mentoring, also reveals inequal conditions in many respects. Concerning role assignments, no significant evidence of sexual discrimination was found. Discrimination against women in assigning core roles such as political and economic news reporting and drama producing, however, was still prevailing. Currently, a marked increase of female proportion is observed among newly hired reporters, producers, and announcers. In the long term, these trends can bring positive changes in the status of female employees in the broadcasting industry organization.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.4
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• pp.79-95
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• 2017

This Study Investigates Whether R&D Capabilities and Market Orientation have Significant Effects on Product Innovation Performance in Order to Enhance Customer Value and Technology Innovation Competitiveness Considering the Characteristics of Rapidly Changing Semiconductor Industry. In other Words, as in the Research Model, the Purpose of this Study is to Investigate the Causal Relationship between the Independent Variable, R&D Capability and Market Orientation, on Product Innovation Performance, which is a Dependent Variable, through the Moderating Variable of Technological Innovation Orientation. For this Study, we Conducted a Questionnaire Survey on the Employees of Development Companies in the Semiconductor Industry and Finally Collected 118 Valid Questionnaires. The Collected Data was Analyzed by Multiple Regression Analysis with Demographic Characteristics as Control Variable and Hierarchical Regression Analysis was Conducted with the Moderating Effects of Technological Innovation Orientation. The Results Showed that the Higher the R&D Intensity and the External Network Capacity, the Higher the Product Innovation Performance. Also, the Product Innovation Performance was Higher than the Customer Orientation and Competitor Orientation Among the Market Orientation. In Addition, only R&D Capability Confirms that Technology Innovation Orientation is Moderated. The Result of this Study is to Improve Understanding of R&D Capability and Market Orientation in Creating of Product Innovation Performance of Semiconductor Companies and to offer Valuable Research Data in Empirically Supporting that Technological Innovation Orientation is an Important Moderating Factor in Creating Firm's Product Innovation Performance and Sustainable Competitive Advantage.

• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.183-194
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• 2001

The centralized traffic information system is to gather and analyze real-time traffic information, to receive traffic information request from user, and to send user processed traffic information such as a path finding. Position information, result of destination search, and other information. In the centralized traffic information system, a server received path-finding requests from many clients and must process clients requests in time. The algorithm of multiple path-finding is needed for a server to process clients request, effectively in time. For this reason, this paper presents a heuristic algorithm that decreases time to compute path-finding requests. This heuristic algorithm uses results of the neighbor nodes shortest path-finding that are computed periodically. Path-finding results of this multiple path finding algorithm to use results of neighbor nodes shortest path-finding are the same as a real optimal path in many cases, and are a little different from results of a real optimal path in non-optimal path. This algorithm is efficiently applied to the general topology and the hierarchical topology such as traffic network. The computation time of a path-finding request that uses results of a neighbor nodes shortest path-finding is 50 times faster than other algorithms such as one-to-one label-setting and label-correcting algorithms. Especially in non-optimal path, the average error rate is under 0.1 percent.

• Journal of the Korea Convergence Society
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• v.10 no.10
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• pp.153-160
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• 2019

In this study, The purpose of this study is to analyze how the rating scale and review contents attributes of social network-based services and products affect consumer purchasing patterns. information provided by screening the main factors. These analyzes are closely and quickly integrated between individuals and businesses, and enable to analyze the transaction that the impact of changing consumers on consumption and purchasing through the usefulness and a priori estimates of reviews and ratings at this time when networks and smart technologies are involved in a wide range of consumer activities. For this study, hierarchical analysis (AHP) and delphi (Delphi) methods applied to classify the high end variables into usefulness, technicality and value, Each subvariable was grouped into three factors and analyzed for importance through evaluation weights. As a result, we could analyze the importance of durability, usefulness, technological innovation, and cost and quality of value. Therefore, this study is expected to provide supplementary and additional useful information to consumers and companies participating in economic activities in various ways by simultaneously analyzing the review score and the reliability of posting information provided by verifying the main factors.

• Korean Journal of Labor Studies
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• v.23 no.2
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• pp.109-139
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• 2017

In light that solidarity movements become significant under the situation of 'labor and labor movement' crisis, our study pays attention to the fact that the Hope Bus campaigns made remarkable achievements, and aims to figure out how those campaigns were successful, by focusing on their solidarity networking. The successful operating conditions of solidarity networking in the Hope Bus campaigns are examined in three aspects - the conditions of triggering, forming, and activating. The solidarity networking of Hope Bus campaigns were mainly triggered by the injustice of layoffs by Hanjin Heavy Industry, aerial protest by Jin-sook Kim, and the tragic symbol of the protest site (Crane no. 85). The solidarity movement of Hope Bus could be formed by the mutual trust and cohesive team-building of key network brokers, their utilization and expansion of social movement networks, and massive ripple effect of SNS-mediated communication. The solidarity networking of Hope Bus was effectively activated by open and de-hierarchical operations of the central planning group, active solidarity activities of participant groups, and the provision of 'heuristic experience' for developing the sensibility to labor solidarity. The virtuous combination of those three operating conditions leads to the building of unified forces among social movements, massive civil participation, and meaningful movement outcomes, through the solidarity networking of Hope Bus campaigns.

• Journal of the Korean Society of Earth Science Education
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• v.15 no.2
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• pp.285-298
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• 2022

This study is to investigate the effects and characteristics of science and engineering integrated lessons on elementary students' creative engineering problem solving propensity (CEPSP). The science and engineering integrated lessons used in this study was a 10 lesson-hours STEM program, co-developed by University of Minnesota and Purdue University. The program was implemented in the 6th grade science class of H Elementary School located in P Metropolitan city. The main data of this study are the pre-post CEPSP result and interview with 5 students collected before and after the research. The CEPSP result was analyzed by a paired-sample t-test and hierarchical cluster analysis. As a result of the t-test, it was found that overall, the program has a positive effect on the students' CEPSP score. As a result of cluster analysis, it was confirmed that studnets' CEPSP could be classified into two groups (lower and higher score cluster). Five students whose, CEPSP score has significantly improved after the lessons were interviewed to find out what the characteristics of the program that contribute the significant change are. As a result of conducting centroid analysis of the interview transcription and the hybrid analysis method, it was found that the meaningful experiences that the five students commonly shared were 'problem solving through collaboration' and 'through repeated experiments (redesign)', problem solving' and 'utilization of scientific knowledge'. As minor reactions, 'choice of the best experimental method' and 'difference between science and engineering' appeared.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.314-328
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• 2023

In this paper, we propose a texture map compression method based on the hierarchical coding method of SHVC to support the scalability function of dynamic mesh compression. The proposed method effectively eliminates the redundancy of multiple-resolution texture maps by downsampling a high-resolution texture map to generate multiple-resolution texture maps and encoding them with SHVC. The dynamic mesh decoder supports the scalability of mesh data by decoding a texture map having an appropriate resolution according to receiver performance and network environment. To evaluate the performance of the proposed method, the proposed method is applied to V-DMC (Video-based Dynamic Mesh Coding) reference software, TMMv1.0, and the performance of the scalable encoder/decoder proposed in this paper and TMMv1.0-based simulcast method is compared. As a result of experiments, the proposed method effectively improves in performance the average of -7.7% and -5.7% in terms of point cloud-based BD-rate (Luma PSNR) in AI and LD conditions compared to the simulcast method, confirming that it is possible to effectively support the texture map scalability of dynamic mesh data through the proposed method.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.323-338
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• 2023

TV advertising with deep analysis of watching pattern of audiences is important to set-top box audience targeting. Applying session-based recommendation model(SBR) to internet commercial, or recommendation based on searching history of user showed its effectiveness in previous studies, but applying SBR to the TV advertising was difficult in South Korea due to data unavailabilities. Also, traditional SBR has limitations for dealing with user preferences, especially in data with user identification information. To tackle with these problems, we first obtain set-top box data from three major broadcasting companies in South Korea(SKB, KT, LGU+) through collaboration with Korea Broadcast Advertising Corporation(KOBACO), and this data contains of watching sequence of 4,847 anonymized users for 6 month respectively. Second, we develop personalized session-based recommendation model to deal with hierarchical data of user-session-item. Experiments conducted on set-top box audience dataset and two other public dataset for validation. In result, our proposed model outperformed baseline model in some criteria.