• Journal of the Korean Society for information Management
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• v.36 no.3
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• pp.149-174
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• 2019

This study analyzes the actual use data of the websites of university libraries, analyzes the users' usage behavior, and proposes improvement measures for the websites. The study analyzed users' traffic and analyzed their usage behavior from January 2018 to December 2018 on the C University website. The website's analysis tool used 'Google Analytics'. The web traffic variables were analyzed in five categories: user general characteristics, user environment analysis, visit analysis, inflow analysis, site analysis, and site analysis based on the metrics of sessions, users, page views, pages per session, average session time, and bounce rate. As a result, 1) In the analysis results of general characteristics of users, there was some access to the website not only in Korea but also in China. 2) In the user experience analysis, the main browser type appeared as Internet Explorer. The next place was Chrome, with a bounce rate of Safari, third and fourth, double that of the Explore or Chrome. In terms of screen resolution, 1920x1080 resolution accounted for the largest percentage, with access in a variety of other environments. 3) Direct inflow was the highest in the inflow media analysis. 4) The site analysis showed the most page views out of 4,534,084 pages, followed by the main page, followed by the lending/extension/history/booking page, the academic DB page, and the collection page.

• Journal of Korea Water Resources Association
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• v.53 no.6
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• pp.409-416
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• 2020

An extreme climate monitoring is essential to the reduction of socioeconomic damages from extreme events. The objective of this study was to produce the near-realtime weekly root-zone Soil Moisture Index (SMI) on the basis of soil moisture using the Noah 3.3 Land Surface Model (LSM) for potentially monitoring extreme drought events. The Yangtze basin was selected to evaluate the Noah LSM performance for the East Asia region (15-60°N, 70-150°E) and the evapotranspiration (ET) and sensible heat flux (SH) were compared with ET and SH from FluxNet and with ET from FluxCom, Global Land Evaporation Amsterdam Model (GLEAM), ERA-5, and Generalized Complementary Relationship (GCR). For the ET, the coefficients of determination (R²) were higher than 0.96, while the R² value for the SH was 0.71 with slightly lower than those. A time series of the weekly root-zone SMI revealed that the regions with Extreme drought had been expanded from the northern part of East China to the entire East China between July to October 2019. The trend analysis of the number of extreme drought events showed that extreme drought events in spring had reduced in South Korea over the past 20 years, while those in fall had a tendency to increase. It is concluded that this study can be useful to reduce the socioeconomic damages resulted from climate extremes by comprehensively characterizing extreme drought events.

• Journal of the Korea Society for Simulation
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• v.24 no.2
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• pp.31-39
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• 2015

Unemployment is related to social issues as well as personal economics activity so various policies have been made to reduce the unemployment rate in many countries. Because of delay inherent in the survey mechanism to collect unemployment data, it takes lots of time to acquire survey unemployment data. To develop proper policies for reducing unemployment rate at the right time, it is quite critical to obtain faster and more accurate information concerning about unemployment level. To remedy this problem, recently an advanced analytics utilizing internet queries is suggested. To examine the potential of Web query information, this research investigates the usefulness of internet activity data to predict Korean unemployment rate. One of selected web-query data(unemployment claim) has a quite strong correlation with unemployment rate. This research employes a time series approach of the ARIMA model that utilizes the information of keyword queries provided by the Naver(Korean representative portal site) trend together with unemployment rate data provisioned from Statistics Korea. With respect to model selection guidelines of mean squared error and prediction error, the model with utilizing the web query information shows better results than the model without such information. This suggests that there is a strong potential for the used method, which needs to be further explored.

• Industrial Engineering and Management Systems
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• v.14 no.4
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• pp.335-346
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• 2015

The electronic commerce site (EC site) has become an important marketing channel where consumers can purchase many kinds of products; their access logs, including purchase records and browsing histories, are saved in the EC sites' databases. These log data can be utilized for the purpose of web marketing. The customers who purchase many product items are good customers, whereas the other customers, who do not purchase many items, must not be good customers even if they browse many items. If the attributes of good customers and those of other customers are clarified, such information is valuable as input for making a new marketing strategy. Regarding the product items, the characteristics of good items that are bought by many users are valuable information. It is necessary to construct a method to efficiently analyze such characteristics. This paper proposes a new latent class model to analyze both purchasing and browsing histories to make latent item and user clusters. By applying the proposal, an example of data analysis on an EC site is demonstrated. Through the clusters obtained by the proposed latent class model and the classification rule by the decision tree model, new findings are extracted from the data of purchasing and browsing histories.

• Journal of Convergence for Information Technology
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• v.11 no.9
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• pp.214-220
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• 2021

With the rapid spread of smartphones, it has become common to listen to music everywhere, just like background music in life, so it is necessary to create a music database that can make recommendations according to individual circumstances and conditions. This paper proposes a music recommendation model through social media. Since emotions, situations, time of day, weather, etc. are included in hashtags, it is possible to build a social media-based database that reflects the opinions of various people with collective intelligence. We use web crawling to collect and categorize different hashtags from posts with music title hashtags to use real listeners' opinions about music in a database. Data from social media is used to create a music database, and music is classified in a different way from collaborative filtering, which is mainly used by existing music platforms.

• Healthcare Informatics Research
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• v.24 no.4
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• pp.253-262
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• 2018

Objectives: We reviewed digital epidemiological studies to characterize how researchers are using digital data by topic domain, study purpose, data source, and analytic method. Methods: We reviewed research articles published within the last decade that used digital data to answer epidemiological research questions. Data were abstracted from these articles using a data collection tool that we developed. Finally, we summarized the characteristics of the digital epidemiological studies. Results: We identified six main topic domains: infectious diseases (58.7%), non-communicable diseases (29.4%), mental health and substance use (8.3%), general population behavior (4.6%), environmental, dietary, and lifestyle (4.6%), and vital status (0.9%). We identified four categories for the study purpose: description (22.9%), exploration (34.9%), explanation (27.5%), and prediction and control (14.7%). We identified eight categories for the data sources: web search query (52.3%), social media posts (31.2%), web portal posts (11.9%), webpage access logs (7.3%), images (7.3%), mobile phone network data (1.8%), global positioning system data (1.8%), and others (2.8%). Of these, 50.5% used correlation analyses, 41.3% regression analyses, 25.6% machine learning, and 19.3% descriptive analyses. Conclusions: Digital data collected for non-epidemiological purposes are being used to study health phenomena in a variety of topic domains. Digital epidemiology requires access to large datasets and advanced analytics. Ensuring open access is clearly at odds with the desire to have as little personal data as possible in these large datasets to protect privacy. Establishment of data cooperatives with restricted access may be a solution to this dilemma.

• Restorative Dentistry and Endodontics
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• v.46 no.3
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• pp.42.1-42.15
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• 2021

Objectives: This study aimed to analyze the main features of the 25 most-cited articles in minimally invasive access cavities. Materials and Methods: An electronic search was conducted on the Clarivate Analytics' Web of Science 'All Databases' to identify the most-cited articles related to this topic. Citation counts were cross-matched with data from Elsevier's Scopus and Google Scholar. Information about authors, contributing institutions and countries, year and journal of publication, study design and topic, access cavity, and keywords were analyzed. Results: The top 25 most-cited articles received a total of 572 (Web of Science), 1,160 (Google Scholar) and 631 (Scopus) citations. It was observed a positive significant association between the number of citations and age of publication (r = 0.6907, p < 0.0001); however, there was no significant association regarding citation density and age of publication (r = -0.2631, p = 0.2038). The Journal of Endodontics made the highest contribution (n = 15, 60%). The United States had the largest number of publications (n = 7) followed by Brazil (n = 4), with the most contributions from the University of Tennessee and Grande Rio University (n = 3), respectively. The highest number of most-cited articles were ex vivo studies (n = 16), and 'fracture resistance' was the major topic studied (n = 10). Conclusions: This study revealed a growing interest for researchers in the field of minimally invasive access cavities. Future trends are focused on the expansion of collaborative networks and the conduction of laboratory studies on under-investigated parameters.

• Culinary science and hospitality research
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• v.23 no.6
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• pp.1-11
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• 2017

The purpose of this study was to compare the cruise tours between China and U.S.A. through the semantic network analysis of big data by collecting online data with SCTM (Smart crawling & Text mining), a data collecting and processing program. The data analysis period was from January $1^{st}$, 2015 to August $15^{th}$, 2017, meanwhile, "cruise tour, china", "cruise tour, usa" were conducted to be as keywords to collet related data and packaged Netdraw along with UCINET 6.0 were utilized for data analysis. Currently, Chinese cruisers concern on the cruising destinations while American cruisers pay more attention on the onboard experience and cruising expenditure. After performing CONCOR (convergence of iterated correlation) analysis, for Chinese cruise tour, there were three clusters created with domestic destinations, international destinations and hospitality tourism. As for American cruise tour, four groups have been segmented with cruise expenditure, onboard experience, cruise brand and destinations. Since the cruise tourism of America was greatly developed, this study also was supposed to provide significant and social network-oriented suggestions for Chinese cruise tourism.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.225-230
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• 2016

Recently, Web has arisen large amount of data that to the development of the network and the Internet. In order to process it appeared that Big Data technology. Big Data technologies have been studied aiming a multifaceted and accurate analysis using existing regular data and a variety of data social data. But social data does not have the expertise and objectivity. And such manipulation and concealment and distortion of information have been raised troubling. Thus, this paper proposes for trust big data platform and will be described in detail. The big data platform proposed in this paper consists of data refiner, Data Analyzer, co-truster, visualizer, searcher, etc.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.5
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• pp.193-201
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• 2011

This paper presents the holistic notion and model of an open social u-learning community, anchored with open content, providing an interactive online study group experience akin to sitting with study buddies on a world-wide campus quad. The interactive social u-learning community design helps conceptualize and maximize advantages of ubiquitous environment in learning. The model is enabled by state-of-the-art web technologies; real-time collaboration technologies for a highly interactive experience; intelligent recommender systems to help learners connect with relevant content and other learners; and mining and analytics to assess learner outcomes. Hence, u-learning design is highly scalable yet interactive and engaging.