• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.5
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• pp.868-889
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• 2022

Fashion companies are using a big data approach as a key strategic analysis to predict and forecast sales. This study investigated the effectiveness of the past sales, web search volume, information amount, brand promotion, and the advertising endorser on the sales forecasting model. The study conducted the autoregressive distributed lag (ARDL) time series model using the internal and external social big data of a national fashion brand. Results indicated that the brand's past sales, search volume, promotion, and amount of advertising endorser information amount significantly affected the sales forecast, whereas the brand's advertising endorser search volume and information amount did not significantly influence the sales forecast. Moreover, the brand's promotion had the highest correlation with sales forecasting. This study adds to information-searching behavior theory by measuring consumers' brand involvement. Last, this study provides digital marketers with implications for developing profitable marketing strategies on the basis of consumers' interest in the brand and advertising endorser.

• The Korea Journal of Herbology
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• v.31 no.4
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• pp.93-100
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• 2016

Objectives : The internet is the most common method to investigate information. It is showed that 75.2% of Internet users of 20s had health information search experience. So this study is aim to understanding of interest of public about the herbal medicine using internet search query volume data.Methods : The Naver that is the top internet portal web service of the Republic of Korea has provided an Internet search query volume data from January 2007 to the current through the Naver data lab (http://datalab.naver.com) service. We have collected search query volume data which was provided by the Naver in 606 herbal medicine names and sorted the data by peak and total search volume.Results : The most frequently searched herbal medicines which has less bias and sorted by peak search volume is 'wasong (와송)'. And the most frequently searched herbal medicines which has less bias and sorted by total search volume is 'hasuo (하수오)'.Conclustions : This study is showed that the rank of interest of public about herbal medicines. Among the above herbal medicines, some herbal medicines had supply issue. And there are some other herbal medicines that had very little demand in Korean medicine market, but highly interested public. So it is necessary to monitor for these herbal medicines which is highly interested of the public. Furthermore if the reliability of the data obtained on the basis of these studies, it is possible to be utilizing herbal medicine monitoring service.

• The Korea Journal of Herbology
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• v.36 no.5
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• pp.1-14
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• 2021

Objectives : From January 2020, when the first confirmed case of COVID-19 in Korea, the use of health information using the Internet is expected to increase. It is expected that there will be a significant change in the general public's interest in Korean herbal medicines for health care. Therefore, in this study, we tried to confirm the change in the search trend of Korean herbal medicines after the COVID-19 epidemic. Methods : Using the "Naver DataLab (http://datalab.naver.com)" service of a Korean portal site Naver, search volume was investigated with 606 Korean herbal medicines as keywords. The search period was from January 2020, right after the onset of COVID-19, to June 2021. The search results were sorted by the peak search volume and the total search volume. Results : 'Cheonsangap (천산갑, 穿山甲, Manitis Squama)' was the most searched Korean herbal medicine in the peak search volume and total search volume with least bias. Conclusions : The problem of supply and demand of Korean herbal medicines of high public interest was identified. Broadcasting and media exposure were the factors that had a big impact on the search volume for Korean herbal medicines. As it was confirmed that the search volume for Korean herbal medicines increased rapidly due to media exposure, it is necessary to provide correct information about Korean herbal medicines, improve public awareness, and manage stable supply and demand based on continuous search trend monitoring.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.81-96
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• 2016

As the internet has become widespread and easy to access everywhere, it is common for people to search information via online search engines such as Google and Naver in everyday life. Recent studies have used online search volume of specific keyword as a measure of the internet users' attention in order to predict disease outbreaks such as flu and cancer, an unemployment rate, and an index of a nation's economic condition, and etc. For stock traders, web search is also one of major information resources to obtain data about individual stock items. Therefore, search volume of a stock item can reflect the amount of investors' attention on it. The investor attention has been regarded as a crucial factor influencing on stock price but it has been measured by indirect proxies such as market capitalization, trading volume, advertising expense, and etc. It has been theoretically and empirically proved that an increase of investors' attention on a stock item brings temporary increase of the stock price and the price recovers in the long run. Recent development of internet environment enables to measure the investor attention directly by the internet search volume of individual stock item, which has been used to show the attention-induced price pressure. Previous studies focus mainly on Dow Jones and NASDAQ market in the United States. In this paper, we investigate the relationship between the individual investors' attention measured by the internet search volumes and stock price changes of individual stock items in the KOSDAQ market in Korea, where the proportion of the trades by individual investors are about 90% of the total. In addition, we examine the difference between industries in the influence of investors' attention on stock return. The internet search volume of stocks were gathered from "Naver Trend" service weekly between January 2007 and June 2015. The regression model with the error term with AR(1) covariance structure is used to analyze the data since the weekly prices in a stock item are systematically correlated. The market capitalization, trading volume, the increment of trading volume, and the month in which each trade occurs are included in the model as control variables. The fitted model shows that an abnormal increase of search volume of a stock item has a positive influence on the stock return and the amount of the influence varies among the industry. The stock items in IT software, construction, and distribution industries have shown to be more influenced by the abnormally large internet search volume than the average across the industries. On the other hand, the stock items in IT hardware, manufacturing, entertainment, finance, and communication industries are less influenced by the abnormal search volume than the average. In order to verify price pressure caused by investors' attention in KOSDAQ, the stock return of the current week is modelled using the abnormal search volume observed one to four weeks ahead. On average, the abnormally large increment of the search volume increased the stock return of the current week and one week later, and it decreased the stock return in two and three weeks later. There is no significant relationship with the stock return after 4 weeks. This relationship differs among the industries. An abnormal search volume brings particularly severe price reversal on the stocks in the IT software industry, which are often to be targets of irrational investments by individual investors. An abnormal search volume caused less severe price reversal on the stocks in the manufacturing and IT hardware industries than on average across the industries. The price reversal was not observed in the communication, finance, entertainment, and transportation industries, which are known to be influenced largely by macro-economic factors such as oil price and currency exchange rate. The result of this study can be utilized to construct an intelligent trading system based on the big data gathered from web search engines, social network services, and internet communities. Particularly, the difference of price reversal effect between industries may provide useful information to make a portfolio and build an investment strategy.

• The Research Journal of the Costume Culture
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• v.26 no.2
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• pp.251-263
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• 2018

As mobile shopping has increased in the new media age, fashion consumers' decision making and product consumption processes have changed. The volume of consumer-driven information has expanded since media and social networking sites have enabled consumers to share information they obtain. The purpose of this study was to determine the factors affecting information searching strategies and information sharing about fashion products. An online survey collected data from 466 respondents, relating to the influence of product price level and consumer SNS commitment level on information search and information sharing. Experimental design of three product price level and two consumer SNS commitment level was used. Analysis of the data identified factors in fashion information searching as ongoing searching, prepurchase web portal information search, and prepurchase marketing information search. For low-price fashion products, prepurchase product-detail influenced intention to share information. For mid-priced products, ongoing search significantly affected intention to share information. Both ongoing search and prepurchase marketing information search showed significant effects for high-price products. Consumers who are more committed to SNS engaged in significantly more searching in all aspects of information search factors. Significant interaction effect was detected for consumer SNS commitment level and product price level. When consumers with low consumer SNS commitment search for information on lower-priced fashion products, they are less likely do a prepurchase web portal information search.

• Interdisciplinary Bio Central
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• v.3 no.2
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• pp.7.1-7.6
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• 2011

Background: Published manuscripts are the main source of biological knowledge. Since the manual examination is almost impossible due to the huge volume of literature data (approximately 19 million abstracts in PubMed), intelligent text mining systems are of great utility for knowledge discovery. However, most of current text mining tools have limited applicability because of i) providing abstract-based search rather than sentence-based search, ii) improper use or lack of ontology terms, iii) the design to be used for specific subjects, or iv) slow response time that hampers web services and real time applications. Results: We introduce an advanced text mining system called PubMine that supports intelligent knowledge discovery based on diverse bio-ontologies. PubMine improves query accuracy and flexibility with advanced search capabilities of fuzzy search, wildcard search, proximity search, range search, and the Boolean combinations. Furthermore, PubMine allows users to extract multi-dimensional relationships between genes, diseases, and chemical compounds by using OLAP (On-Line Analytical Processing) techniques. The HUGO gene symbols and the MeSH ontology for diseases, chemical compounds, and anatomy have been included in the current version of PubMine, which is freely available at http://pubmine.kobic.re.kr. Conclusions: PubMine is a unique bio-text mining system that provides flexible searches and analysis of biological entity relationships. We believe that PubMine would serve as a key bioinformatics utility due to its rapid response to enable web services for community and to the flexibility to accommodate general ontology.

• Journal of KIISE
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• v.42 no.3
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• pp.390-398
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• 2015

Spatial web objects refer to web documents that contain geographic information. Recently, services that create spatial web objects have increased greatly because of the advancements in devices such as smartphones. For services such as Twitter or Facebook, simple texts posted by users is stored along with information about the post's location. To search for such spatial web objects, a method that uses spatial information and text information simultaneously is required. Conventional spatial web object search methods mostly use R-tree and inverted file methods. However, these methods have a disadvantage of requiring a large volume of space when building indices. Furthermore, such methods are efficient for searching with many keywords but are inefficient for searching with a few keywords.. In this paper, we propose a spatial web object search method that uses a quad-tree and a patricia-trie. We show that the proposed technique is more effective than existing ones in searching with a small number of keywords. Furthermore, we show through an experiment that the space required by the proposed technique is much smaller than that required by existing ones.

• Review of Culture and Economy
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• v.20 no.2
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• pp.119-147
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• 2017

This study analyzes Big Data to understand the economic influence of K-Beauty which is expected as a fast-growing industry. Because the content of K-beauty is mainly transmitted over the Internet, Big Data about K-Beauty in the database of online services can show interest and engagement in K-Beauty. The export volume of the beauty industry and the number of foreign tourist in Korea were used as dependent variables. The volume of Google search and the volume of YouTube page view were independent variables. According to the result of a multi-regression analysis, the volume of Google search of K-Beauty had a positive influence on both dependent variables, even after controlling for GDP (Gross Domestic Product) and distances between nations. When it comes to the volume of YouTube page view of K-Beauty, it had a positive relationship with the export volume of the beauty industry, whereas there was no significant relationship between the volume of YouTube page view and the number of foreign tourists. The result indicates that the content of K-Beauty has a significant impact on the beauty industry. Moreover, this empirical study shows that web search and YouTube search have a positive relationship with the economical aspect. These results can be used to discuss public relations strategy to promote K-Beauty industry.

• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1601-1614
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• 2008

The number of web service users has been increased rapidly as existing services are changed into the web-based internet applications. Therefore, it is necessary for us to use web log pre-processing technique to detect attacks on diverse web service transactions and it is also possible to extract web mining information. However, existing mechanisms did not provide efficient pre-processing procedures for a huge volume of web log data. In this paper, we proposed both a field based parsing and a high-speed log indexing mechanism based on the suggested B-tree Index Vector structure for performance enhancement. In experiments, the proposed mechanism provides an efficient web log pre-processing and search functions with a session classification. Therefore it is useful to enhance web attack detection function.

• The Journal of Korean Academy of Prosthodontics
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• v.59 no.2
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• pp.164-172
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• 2021

Purpose: The purpose of this study was to analyze the level of interest that common Internet users have in dental implant using a Google Trends, and to compare the level of interest with big data from National Health Insurance Service. Materials and methods: Google Trends provides a relative search volume for search keywords, which is the average data that visualizes the frequency of searches for those keywords over a specific period of time. Implant was selected as the search keyword to evaluate changes in time flows of general Internet users' interest from 2015 to 2019 with trend line and 6 month moving average. Relative search volume for implant was analyzed with the number of patients who received National Health Insurance coverage for implant. Interest in implant and conventional denture was compared and popular related search keywords were analyzed. Results: Relative search volume for implant has increased gradually and showed a significant positive correlation with the total number of patients (P<.01). Interest in implant was higher than denture for most of the time. Keywords related to implant cost were most frequently observed in all years and related search on implant procedure was increasing. Conclusion: Within the limitations of this study, the public interest in dental implant was gradually increasing and specific areas of interest were changing. Web-based Google Trends data was also compared with traditional data and significant correlation was confirmed.