• Culinary science and hospitality research
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• v.23 no.4
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• pp.22-32
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• 2017

The purpose of this study was to explore awareness of food tourism using big data analysis. For this, this study collected data containing 'food tourism' keywords from google web search, google news, and google scholar during one year from January 1 to December 31, 2016. Data were collected by using SCTM (Smart Crawling & Text Mining), a data collecting and processing program. From those data, degree centrality and eigenvector centrality were analyzed by utilizing packaged NetDraw along with UCINET 6. The result showed that the web visibility of 'core service' and 'social marketing' was high. In addition, the web visibility was also high for destination, such as rural, place, ireland and heritage; 'socioeconomic circumstance' related words, such as economy, region, public, policy, and industry. Convergence of iterated correlations showed 4 clustered named 'core service', 'social marketing', 'destinations' and 'social environment'. It is expected that this diagnosis on food tourism according to changes in international business environment by using these web information will be a foundation of baseline data useful for establishing food tourism marketing strategies.

• The Journal of Bigdata
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• v.3 no.1
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• pp.63-71
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• 2018

In the last five years (2013~2017), we analyzed what factors influenced Korean films that have surpassed 10 million viewers in the Korean movie industry, where the total number of moviegoers is over 200 million. In general, many people consider the number of screens and ratings as important factors that affect the audience's success. In this study, four additional factors, including the number of screens and ratings, were established to establish a hypothesis and correlate it with the presence of 10 million spectators through big data analysis. The results were significant, with 91 percent accuracy in predicting 10 million viewers and 99.4 percent accuracy in estimating cumulative attendance.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.1
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• pp.59-69
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• 2019

The auditor, an important instrument of corporate governance, ensures the transparency and accountability of the firm to the stakeholders. The objective of this paper is to explore the impact of audit characteristics on firm performance. In this study, external audit quality (BIG4), frequencies of audit committee meetings, and audit committee size are used as the proxies of audit characteristics and firm performance is measured through ROA, profit margin and EPS. A total of 503 firm years are considered as sample size from the listed manufacturing firms of Dhaka Stock Exchange (DSE) during the period of 2013 to 2017 to find out the impact of audit characteristics on firm performance. In this study, multivariate regression analysis is conducted using the pooled OLS method. Moreover, time dummy and lag model of multivariate analysis are also analyzed as robust check. The multivariate regression results find that external audit quality (BIG4) and audit committee size are significantly positively associated with firm performance. This study also finds that there is a significant negative relationship between audit committee meeting and firm performance. This study recommends that the regulatory authority and audit committee should review the frequencies of audit committee meeting to make it more effective to ensure better firm performance.

• Asia-Pacific Journal of Business
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• v.12 no.3
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• pp.193-212
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• 2021

Purpose - This study investigates the effect of auditor change on client firm's accounting (financial statement) comparability. Design/methodology/approach - The comparability of accounting information is measured by the difference in accruals between the two firms. Additionally, the study uses earnings-stock return relationship as another proxy of accounting comparability. In particular, the paper examines whether there is a systematic difference between initial audit years and the other years with respect to the client firm's accounting comparability. Moreover, current study tests how changes in auditor size or industry expertise before and after the switch of auditors affect the accounting comparability. Findings - The results show that the level of accounting comparability is lower in the year of auditor change than in the other years. Furthermore, this lower level of comparability is derived by the observations that switch their auditors from non-Big4 to non-Big4 or from non-specialist to non-specialist. These results are consistent when accounting comparability is measured by different proxies. Research implications or Originality - The findings of this study provide important policy implications for the regulations related with auditor selection.

• Journal of Fashion Business
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• v.27 no.4
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• pp.50-66
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• 2023

The purpose of this study was to examine the word distribution and topic distribution of slow fashion appearing on YouTube in detail and identify the characteristics and aspects related to fashion design through big data analysis and content analysis methods. The specific research results were as follows. First, in the results of the word distribution analysis, "item" appeared the most, 203 times. Also, "one-piece" was a point to pay attention to, as the item had the highest frequency. Second, a total of 5 topics were defined in the topic distribution analysis: topic 1 was "vintage products," topic 2 was "fashion items," topic 3 was "eco-friendly," topic 4 was "life quality emphasis," and topic 5 was "prudent consumption." Third, looking at the relationship between word distribution and topic distribution above, Korean slow fashion on YouTube was actively selecting related design elements that express vintage images in clothing life regardless of trends. In addition, there was a tendency to pursue various basic and high-quality items. Other than those findings, basic items tended to be reinterpreted in various ways through styling methods matched to the vintage image. Lastly, the tendency of slow and small-volume production appeared to emphasize handicrafts and the cultural values of fashion products.

• Journal of Information Technology and Architecture
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• v.11 no.4
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• pp.449-462
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• 2014

In big data era, there are a number of considerable parts in processing systems for capturing, storing, and analyzing stored or streaming data. Unlike traditional data handling systems, a big data processing system needs to concern the characteristics (format, velocity, and volume) of being handled data in the system. In this situation, virtualized computing platform is an emerging platform for handling big data effectively, since virtualization technology enables to manage computing resources dynamically and elastically with minimum efforts. In this paper, we analyze resource utilization of virtualized computing resources to discover suitable deployment models in Apache Hadoop and HBase-based big data processing environment. Consequently, Task Tracker service shows high CPU utilization and high Disk I/O overhead during MapReduce phases. Moreover, HRegion service indicates high network resource consumption for transfer the traffic data from DataNode to Task Tracker. DataNode shows high memory resource utilization and Disk I/O overhead for reading stored data.

• The Journal of Bigdata
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• v.2 no.2
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• pp.75-86
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• 2017

In this study, I propose a method to automate the method to diagnose the quality of big data. The reason for automating the quality diagnosis of Big Data is that as the Fourth Industrial Revolution becomes a issue, there is a growing demand for more volumes of data to be generated and utilized. Data is growing rapidly. However, if it takes a lot of time to diagnose the quality of the data, it can take a long time to utilize the data or the quality of the data may be lowered. If you make decisions or predictions from these low-quality data, then the results will also give you the wrong direction. To solve this problem, I have developed a model that can automate diagnosis for improving the quality of Big Data using machine learning which can quickly diagnose and improve the data. Machine learning is used to automate domain classification tasks to prevent errors that may occur during domain classification and reduce work time. Based on the results of the research, I can contribute to the improvement of data quality to utilize big data by continuing research on the importance of data conversion, learning methods for unlearned data, and development of classification models for each domain.

• Journal of Convergence for Information Technology
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• v.7 no.5
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• pp.177-182
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• 2017

As major ICT technologies such as IoT, cloud computing, and Big Data are being applied to manufacturing, smart factories are beginning to be built. The key of smart factory implementation is the ability to acquire and analyze data of the factory. Therefore, the need for a big data analysis platform is increasing. The purpose of this study is to construct a platform for big data analysis of manufacturing process and propose integrated method for analysis. The proposed platform is a RHadoop-based structure that integrates analysis tool R and Hadoop to distribute a large amount of datasets. It can store and analyze big data collected in the unit process and factory in the automation system directly in HBase, and it has overcome the limitations of RDB - based analysis. Such a platform should be developed in consideration of the unit process suitability for smart factories, and it is expected to be a guide to building IoT platforms for SMEs that intend to introduce smart factories into the manufacturing process.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.7
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• pp.149-154
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• 2014

Modern data computing developments have led to big improvements in graphic capabilities and there are many new possibilities for data displays. The visualization has proven effective for not only presenting essential information in vast amounts of data but also driving complex analyses. Big-data analytics and discovery present new research opportunities to the computer graphics and visualization community. In this paper, we discuss the patent analysis of big data visualization technology development in major countries. Especially, we analyzed 160 patent applications and registered patents in four countries on November 2012. According to the result of analysis provided by this paper, the text clustering analysis and 2D visualization are important and urgent development is needed to be oriented. In particular, due to the increase of use of smart devices and social networks in domestic, the development of three-dimensional visualization for Big Data can be seen very urgent.

• The Journal of Bigdata
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• v.3 no.2
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• pp.11-18
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• 2018

This study is a study on domain automatic classification for domain - based quality diagnosis which is a key element of big data quality diagnosis. With the increase of the value and utilization of Big Data and the rise of the Fourth Industrial Revolution, the world is making efforts to create new value by utilizing big data in various fields converged with IT such as law, medical, and finance. However, analysis based on low-reliability data results in critical problems in both the process and the result, and it is also difficult to believe that judgments based on the analysis results. Although the need of highly reliable data has also increased, research on the quality of data and its results have been insufficient. The purpose of this study is to shorten the work time to automizing the domain classification work which was performed from manually to using machine learning in the domain - based quality diagnosis, which is a key element of diagnostic evaluation for improving data quality. Extracts information about the characteristics of the data that is stored in the database and identifies the domain, and then featurize it, and automizes the domain classification using machine learning. We will use it for big data quality diagnosis and contribute to quality improvement.