• The KIPS Transactions:PartD
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• v.18D no.3
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• pp.157-168
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• 2011

Recently flash memory has been being utilized as a main storage device in mobile devices, and flashSSDs are getting popularity as a major storage device in laptop and desktop computers, and even in enterprise-level server machines. Unlike HDDs, on flash memory, the overwrite operation is not able to be performed unless it is preceded by the erase operation to the same block. To address this, FTL(Flash memory Translation Layer) is employed on flash memory. Even though the modified data block is overwritten to the same logical address, FTL writes the updated data block to the different physical address from the previous one, mapping the logical address to the new physical address. This enables flash memory to avoid the high block-erase cost. A flashSSD has an array of NAND flash memory packages so it can access one or more flash memory packages in parallel at once. To take advantage of the internal parallelism of flashSSDs, it is beneficial for DBMSs to request I/O operations on sequential logical addresses. However, the B-tree structure, which is a representative index scheme of current relational DBMSs, produces excessive I/O operations in random order when its node structures are updated. Therefore, the original b-tree is not favorable to SSD. In this paper, we propose AS(Always Sequential) B-tree that writes the updated node contiguously to the previously written node in the logical address for every update operation. In the experiments, AS B-tree enhanced 21% of B-tree's insertion performance.

• The Journal of the Korea Contents Association
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• v.19 no.5
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• pp.82-93
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• 2019

Fashion cannot encourage co-consumption with other people as a personal item, but it can lead to new consumer needs if fashion sharing service can professionally replace the time and cost of purchasing and managing goods. The purpose of this study is to empirically investigate the factors influencing the acceptance of fashion-sharing services based on the integration theory of user acceptance and utilization (UTAUT), and to discuss the virtuous cycle and sustainability pursuit of resources through the activation of the sharing. In this study, the research model for the acceptance of fashion sharing applications is schematized, and the survey was conducted 300 women aged 20~49 years. The screens of 'Project Anne', a representative fashion sharing service in Korea, were provided as a visual data. Reliability analysis, correlation analysis, confirmatory factor analysis, structural equation analysis, and multiple group analysis were performed using SPSS 23.0 and AMOS 22.0 statistical package for statistical analysis. As a result, efficiency and social influence positively influenced behavioral intention to use, and age has found that efficiency and social influences modulate the intensity of behavioral intention to use. Therefore, for the consumer acceptance and activation of fashion sharing services, marketing activities emphasizing efficiency and strengthening social influence factors are essential. Also, it is necessary to maintain the existing target group, 30~40s, and also construct additional products and price services for the 20s. This study is of academic significance in presenting basic data for empirical research on consumer acceptance of fashion sharing, and suggests a study on the influence relationship among user experience components for real users in the future.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.99-120
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• 2010

Due to the wide spread of customers' frequent access of non face-to-face services, there have been many attempts to improve customer satisfaction using huge amounts of data accumulated throughnon face-to-face channels. Usually, a call center is regarded to be one of the most representative non-faced channels. Therefore, it is important that a call center has enough agents to offer high level customer satisfaction. However, managing too many agents would increase the operational costs of a call center by increasing labor costs. Therefore, predicting and calculating the appropriate size of human resources of a call center is one of the most critical success factors of call center management. For this reason, most call centers are currently establishing a department of WFM(Work Force Management) to estimate the appropriate number of agents and to direct much effort to predict the volume of inbound calls. In real world applications, inbound call prediction is usually performed based on the intuition and experience of a domain expert. In other words, a domain expert usually predicts the volume of calls by calculating the average call of some periods and adjusting the average according tohis/her subjective estimation. However, this kind of approach has radical limitations in that the result of prediction might be strongly affected by the expert's personal experience and competence. It is often the case that a domain expert may predict inbound calls quite differently from anotherif the two experts have mutually different opinions on selecting influential variables and priorities among the variables. Moreover, it is almost impossible to logically clarify the process of expert's subjective prediction. Currently, to overcome the limitations of subjective call prediction, most call centers are adopting a WFMS(Workforce Management System) package in which expert's best practices are systemized. With WFMS, a user can predict the volume of calls by calculating the average call of each day of the week, excluding some eventful days. However, WFMS costs too much capital during the early stage of system establishment. Moreover, it is hard to reflect new information ontothe system when some factors affecting the amount of calls have been changed. In this paper, we attempt to devise a new model for predicting inbound calls that is not only based on theoretical background but also easily applicable to real world applications. Our model was mainly developed by the interactive decision tree technique, one of the most popular techniques in data mining. Therefore, we expect that our model can predict inbound calls automatically based on historical data, and it can utilize expert's domain knowledge during the process of tree construction. To analyze the accuracy of our model, we performed intensive experiments on a real case of one of the largest car insurance companies in Korea. In the case study, the prediction accuracy of the devised two models and traditional WFMS are analyzed with respect to the various error rates allowable. The experiments reveal that our data mining-based two models outperform WFMS in terms of predicting the amount of accident calls and fault calls in most experimental situations examined.

• Journal of the Korean Institute of Landscape Architecture
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• v.41 no.1
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• pp.51-59
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• 2013

This study was conducted to compare the sizes of effective values, which users recognize, according to the kinds of physical and psychological services provided by an arboretum, by estimating them in monetary values. As an analysis tool for this purpose, the CE(Choice Experiments) method, which is able to estimate effective value's size depending on each variable, was employed. For drawing up profiles for estimation of the values of individual services, 25 profiles were extracted using the orthogonal design of the SPSS statistical package, and questions of 75 pairs were created not to make each of the profiles overlapped. Then, each user was given three questions at five sets each and 3,510 data were used for the analysis. As the result, in relation to the attribute, 'The kinds of trees should be diversified 50% more than now.', firstly, users showed the biggest willingness to pay, based on the present level, and expressed intentions to pay 7,956 won, additionally. Secondly, the value of the path design that was unique than the present road design was estimated in 6,025 won, and when individual attendants guided visitors in the arboretum, they expressed intentions to pay nearly three times more expenses than when they were guided as a group. These results show that users in the Gyeongnam Arboretum recognized the highest effective values towards the collection and display of trees that are arboretum's original functions, and it was followed by the unique road design to observe a variety of dense trees well. This research could be useful in comparing or measuring particular effective values of users that central operators of arboretums want to know. Moreover, it would be suggested as an advanced research for providing basic data about value estimation of individual environmental goods not only in arboretums, but also in other fields.

• Journal of Korea Entertainment Industry Association
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• v.13 no.5
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• pp.263-271
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• 2019

The recent phenomenon of job "Miss Match", which is inconsistent with knowledge in the demand of educational training institutes and industries, has spread to an increase in private education costs for reeducation and employment of new hires, resulting in weak individual job competency and poor employment capability, as well as economic and material waste at the national level. To compensate for these problems, the National Competency Standards(NCS), which are available immediately in practice and look for a standard point of national job competency with the aim of fostering human resources sought by industries, were developed, and even the NCS-based qualification system was launched in line with the stream of times. This study is intended to look into the importance and priority of competency units and competency unit elements at the NCS-based qualification L3 level in the skin care field for an overall check of the NCS-based qualification level at a time when educational institutes are organizing and operating the school curriculums according to the NCS and NCS-based qualification level. And it is attempted to provide basic data for the development of curriculum in fostering professional human resources required by industries. To analyze the needs for competency units and competency unit elements at the L3 level, a survey using AHP method was carried out to a group of field experts and a group of education experts. In addition, the SPSS(Statistical Package for Social Science) ver. 21.0 and Expert Choice 2000, an AHP-only solution was used to do statistical processing through the processes of data coding and data cleaning. The findings showed that there was a partial difference of opinion between a group of field experts and a group of education experts. This indicates that the inconsistencies between educational training institutes and industrial sites should be resolved at this time of change with the aim of fostering field customized human resources with professional skills. Consequently, the solution is to combine jobs at industrial sites and standardized educations of educational institutes with human resources required at industrial sites.

• 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.