• The Journal of the Korea Contents Association
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• v.15 no.11
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• pp.509-523
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• 2015

With the introduction of smart devices as a new channel of information distribution, the mass tourism that has been dominating the travel scene is being transformed into individual tourism. Therefore, it is more than important to establish an advanced smart tourism environment using cutting-edge ICT technologies in order to go into one of tourism developed countries. In line with that, this study draws both local and international cases to show where smart tourism stands now by mapping out problems and solutions by category. Firstly, in terms of infrastructure, establishing distribution platform and big data analyzing systems were suggested. Secondly, to fit the needs of consumers, converged tourism content and user experience based content development are in need. Lastly, in terms of governance forming public-private consultative body and incubating creative tourism companies are suggested. The study results will serve as a fruitful reference to those who want to establish business strategy related to smart tourism.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.23 no.3
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• pp.57-84
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• 2012

With the advent of Big data era unleashed by tremendous increase of structured and unstructured data, a library needs an effective method to store, manage and preserve various information resources. Also, needs of collaboration of libraries are continuously increased in digital environment. As an effective method to handle the changes and challenges in libraries, interest on cloud computing is growing more and more. This study aims to propose a method to introduce cloud computing in libraries. To achieve the goals, this study performed the literature review to analyze problems of existing library systems. Also, this study proposed considerations, expectations, service scenario, phased strategy to introduce cloud computing in libraries. Based on the results extracted from cases that libraries have introduced cloud computing-based systems, this study proposed introduction strategy and specific applying areas in library works as considered features of cloud computing models. The proposed phased strategy and service scenario may reduce time and effort in the process of introduction of cloud computing and maximize the effect of cloud computing.

• Journal of Convergence for Information Technology
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• v.11 no.4
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• pp.35-45
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• 2021

The e-commerce industry using mobile or web is growing rapidly, and the emergence of various platform services is causing innovative changes in the e-commerce industry. This study aims to identify the attachment types of e-commerce users and to demonstrate the relationship between the PPerceived Usefulness, and Perceived Ease of Use by TAM. In order to empirically verify the research model of this study, a survey was conducted on ordinary people with experience using e-commerce and path analysis was conducted by using PLS to analyze its Internal consistency, Confirmatory factor analysis, Discriminant validity and Goodness-of-fit verification. As a result, a significant relationship between Perceived Stability, Perceived Usefulness, and Perceived Ease of Use was identified, could verify the association with the TAM and Acceptance Intention.

• Journal of Advanced Technology Convergence
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• v.2 no.2
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• pp.1-8
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• 2023

Due to the recent long-term economic downturn, the number of non-store and mail-order sellers is increasing as prospective entrepreneurs are concentrated due to the phenomenon of non-store start-ups with low start-up costs. In particular, in addition to unemployed young people and housewives who lack funds, many office workers who wish to have a 'two-job' are jumping into the business. Therefore, in this paper, we have moved away from provider-oriented service platforms that are dependent on specific networks, operators, and service types. In addition, we plan to design a business integration support system that can provide B2B services in the promotional material industry that contributes to business support and profit generation of wholesale and retail non-store entrepreneurs through sharing and participation. The proposed system is judged to be a business integrated operation support system applying orchestration and service management technology and enterprise business partner management technology that can provide stable operation management service.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.33-42
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• 2021

As smartphone-based tourism platforms have become active, policy establishment and service enhancement using review data are being made in various fields. In the case of the preceding studies using tourism review data, most of the studies centered on domestic tourists were conducted, and in the case of foreign tourist studies, studies were conducted only on data collected in some languages and text mining techniques. In this study, 3,515 review data written by foreigners were collected by designating the "Daegu attractions" keyword through the online review site. And LDA-based topic modeling was performed to derive tourism topics. The spatial approach through global and local spatial autocorrelation analysis for each topic can be said to be different from previous studies. As a result of the analysis, it was confirmed that there is a global spatial autocorrelation, and that tourist destinations mainly visited by foreigners are concentrated locally. In addition, hot spots have been drawn around Jung-gu in most of the topics. Based on the analysis results, it is expected to be used as a basic research for spatial analysis based on local government foreign tourism policy establishment and topic modeling. And The limitations of this study were also presented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.535-544
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• 2018

As the spread of mobile devices equipped with various sensors and high-quality wireless network communications functionsexpands, the amount of spatio-temporal data generated from mobile devices in various service fields is rapidly increasing. In conventional research into processing a large amount of real-time spatio-temporal streams, it is very difficult to apply a Hadoop-based spatial big data system, designed to be a batch processing platform, to a real-time service for spatio-temporal data streams. This paper extends the MapReduce online framework to support real-time query processing for continuous-input, spatio-temporal data streams, and proposes a load management method to distribute overloads for efficient query processing. The proposed scheme shows a dynamic load balancing method for the nodes based on the inflow rate and the load factor of the input data based on the space partition. Experiments show that it is possible to support efficient query processing by distributing the spatial data stream in the corresponding area to the shared resources when load management in a specific area is required.

• The Journal of Bigdata
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• v.4 no.2
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• pp.23-34
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• 2019

Recently, the crime that utilizes the digital platform is continuously increasing. About 140,000 cases occurred in 2015 and about 150,000 cases occurred in 2016. Therefore, it is considered that there is a limit handling those online crimes by old-fashioned investigation techniques. Investigators' manual online search and cognitive investigation methods those are broadly used today are not enough to proactively cope with rapid changing civil crimes. In addition, the characteristics of the content that is posted to unspecified users of social media makes investigations more difficult. This study suggests the site-based collection and the Open API among the content web collection methods considering the characteristics of the online media where the infringement crimes occur. Since illegal content is published and deleted quickly, and new words and alterations are generated quickly and variously, it is difficult to recognize them quickly by dictionary-based morphological analysis registered manually. In order to solve this problem, we propose a tokenizing method in the existing dictionary-based morphological analysis through WPM (Word Piece Model), which is a data preprocessing method for quick recognizing and responding to illegal contents posting online infringement crimes. In the analysis of data, the optimal precision is verified through the Vote-based ensemble method by utilizing a classification learning model based on supervised learning for the investigation of illegal contents. This study utilizes a sorting algorithm model centering on illegal multilevel business cases to proactively recognize crimes invading the public economy, and presents an empirical study to effectively deal with social data collection and content investigation.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.67-75
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• 2022

Medical information is variously generated not only from medical devices but also from electronic devices. Recently, related convergence technologies from big data collection in healthcare to medical AI products for patient's condition analysis are rapidly increasing. However, there are difficulties in applying them because of independent developmental procedures. In this paper, we propose an intelligent hospital information system (iHIS) model to simplify and integrate research, development and application of medical AI technology. The proposed model includes (1) real-time patient data management, (2) specialized data management for medical AI development, and (3) real-time monitoring for patient. Using this, real-time biometric data collection and medical AI specialized data generation from patient monitoring devices, as well as specific AI applications of camera-based patient gait analysis and brain MRA-based cerebrovascular disease analysis will be introduced. Based on the proposed model, it is expected that it will be used to improve the HIS by increasing security of data management and improving practical use through consistent interface platformization.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.171-189
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• 2022

Online reviews play an essential role in the consumer's purchasing decision-making process, and thus, providing helpful and reliable reviews is essential to consumers. Previous online review helpfulness prediction studies mainly predicted review helpfulness based on the consistency of text and rating information of online reviews. However, there is a limitation in that representation capacity or review text and rating interaction. We propose a CNN-RHP model that effectively learns the interaction between review text and rating information to improve the limitations of previous studies. Multi-channel CNNs were applied to extract the semantic representation of the review text. We also converted rating into independent high-dimensional embedding vectors representing the same dimension as the text vector. The consistency between the review text and the rating information is learned based on element-wise operations between the review text and the star rating vector. To evaluate the performance of the proposed CNN-RHP model in this study, we used online reviews collected from Amazom.com. Experimental results show that the CNN-RHP model indicates excellent performance compared to several benchmark models. The results of this study can provide practical implications when providing services related to review helpfulness on online e-commerce platforms.

• Information Systems Review
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• v.23 no.3
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• pp.51-75
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• 2021

Global e-commerce websites offer personalized recommendation services to gain sustainable competitiveness. Existing studies have offered personalized recommendation services using quantitative preferences such as ratings. However, offering personalized recommendation services using only quantitative data has raised the problem of decreasing recommendation performance. For example, a user gave a five-star rating but wrote a review that the user was unsatisfied with hotel service and cleanliness. In such cases, has problems where quantitative and qualitative preferences are inconsistent. Recently, a growing number of studies have considered review data simultaneously to improve the limitations of existing personalized recommendation service studies. Therefore, in this study, we identify review and rating mismatches and build a new user profile to offer personalized recommendation services. To this end, we use deep learning algorithms such as CNN, LSTM, CNN + LSTM, which have been widely used in sentiment analysis studies. And extract sentiment features from reviews and compare with quantitative preferences. To evaluate the performance of the proposed methodology in this study, we collect user preference information using real-world hotel data from the world's largest travel platform TripAdvisor. Experiments show that the proposed methodology in this study outperforms the existing other methodologies, using only existing quantitative preferences.