• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.189-195
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• 2018

With the advent of the digital age, production and distribution of web pages has been exploding. Internet users frequently need to extract specific information they want from these vast web pages. However, it takes lots of time and effort for users to find a specific information in many web pages. While search engines that are commonly used provide users with web pages containing the information they are looking for on the Internet, additional time and efforts are required to find the specific information among extensive search results. Therefore, it is necessary to develop algorithms that can automatically extract specific information in web pages. Every year, thousands of international conference are held all over the world. Each international conference has a website and provides general information for the conference such as the date of the event, the venue, greeting, the abstract submission deadline for a paper, the date of the registration, etc. It is not easy for researchers to catch the abstract submission deadline quickly because it is displayed in various formats from conference to conference and frequently updated. This study focuses on the issue of extracting abstract submission deadlines from International conference websites. In this study, we use three machine learning models such as SVM, decision trees, and artificial neural network to develop algorithms to extract an abstract submission deadline in an international conference website. Performances of the suggested algorithms are evaluated using 2,200 conference websites.

• Journal of the Korean Society for information Management
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• v.18 no.1
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• pp.43-64
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• 2001

The purpose of this study is to examine the features of Internet bookshops in order to suggest the most effective website model for them. Seven Internet bookshop websites are analyzed. The evaluating criteria includes: interface layout, contents, function aspects. The results show that a suggested model should include various aspects: 1) user-centered menu structure, 2) effective search methodlsubject directory, and 3) various and good quality review.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1564-1573
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• 2016

With the spread of social media and mobile devices, people spend more time on online than ever before. As more people participate in various online activities, much research has been conducted on how to make use of the time effectively and productively. In this paper, we propose two methods which can be used to extract highlights and make searchable media indexes using online social data. For highlight extraction, we collected the comments from the online baseball broadcasting website. We adopted peak-finding algorithm to analyze the frequency of comments uploaded on the comments section of the website. For each indexes, we collected postings from soap opera forums provided by a popular web service called DCInside. We extracted all the instances when a character's name is mentioned in postings users upload after watching TV, which can be used to create indexes when the character appears on screen for the given episode of the soap opera The evaluation results shows the possibility of the crowdsourcing-based media interaction for both highlight extraction and index building.

• The Journal of Industrial Distribution & Business
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• v.8 no.6
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• pp.7-16
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• 2017

Purpose - Today's world revolves around search engines which are the driving force behind any marketer. The thirst for marketing has led to the evolution of online 'Pay per click' over last few years and is the most widely used instrument. Research design, data, and methodology - Exploratory research design highlights many marketing variables getting affected by pay per click marketing. To analyze the said phenomenon, the data was gathered through questionnaire from the sample of 338 respondents which were selected by simple random sampling method mostly from the National Capital Region (NCR) of Delhi in India. The data collected from the respondents was loaded on SAS base for exploratory factor analysis and multiple regression analysis. Results - Pay per click as a marketing tool has significant impact on the consumers. The most prominent factors of pay per click marketing identified in the research are Ad quality, Competition, Targeting, Trend and Budget. Conclusions - Organic as well as inorganic ads, keeping in mind the end goal to gage the exchange of these two postings in the marked look territory. Additionally, here we dissected supported pursuit promotions in all. It would be beneficial to break down the impact of promotion position on the pay per click marketing.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.21 no.3
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• pp.45-56
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• 2010

This study examines the status of websites operation among libraries affiliated to the Korean Medical Library Association, compares and analyzes website menu organization and information service contents of 29 medical libraries that currently run websites. The followings are results of this study. 1) the main menu of medical library websites is arranged with 'Search for Library Materials, Electronic Resources, Library Service, My Library, Library Guide, and the Bulletin Board.' 2) 15 services were selected as the main information service provided from the medical library website. 3) the information service that was provided from the most institutions was 'Document Copy/ Interlibrary Loan,' followed by 'Loan Reservation/Renewal, Request Library Purchase Book, Off-campus Access, User Education, Reference Resources, Q&A Board, Our Library Pubmed, and Dissertation Submission' in this order.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.13 no.1
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• pp.59-75
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• 2002

This study tried to evaluate the usability of web-based academic library services delivered to users in terms of effectiveness, efficiency, and user satisfaction. 13 subjects were randomly selected on the voluntary basis in Duksung Women's University Library from Dec. 15, 2001 to Feb. 27, 2002. They were given six kinds of tasks related to the use of library services through the library website. 3.6 tasks out of 6 were given right answers by subjects, with success rate of 60% and average spent time of 2 minutes and 22 seconds. The most effective library services on the website was a book search with success rate of 100%. However the least effective service was journal article search with success rate of 23.1% . The most efficient library service on the web was a book search. 100% of subjects found the answer in 42.2 seconds, while the least efficient library service was journal article search in which 23.1% of subjects found the answer in 271.7 seconds. Subjects were highly satisfied with the book request service on the web since it is the easiest to understand and the most convenient to use.

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.149-157
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• 2010

As the use of the Internet has recently increased, the demand for opinion information posted on the Internet has grown. However, such resources only exist on the website. People who want to search for information on the Internet find it inconvenient to visit each website. This paper focuses on the opinion information extraction and analysis system through Web mining that is based on statistics collected from Web contents. That is, users' opinion information which is scattered across several websites can be automatically analyzed and extracted. The system provides the opinion information search service that enables users to search for real-time positive and negative opinions and check their statistics. Also, users can do real-time search and monitoring about other opinion information by putting keywords in the system. Proposed technologies proved to have outstanding capabilities in comparison to existing ones through tests. The capabilities to extract positive and negative opinion information were assessed. Specifically, test movie review sentence testing data was tested and its results were analyzed.

• Journal of Digital Convergence
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• v.17 no.7
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• pp.245-254
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• 2019

The purpose of this study is to develop portal web contents for nursing student's career search. The portal web contents named 'Nurse Dream' was designed and developed by using 134 nursing students' survey data and referring to expert consultation. 97.0% of respondents expressed intention of using website or application for career search if it is completed. What they want to know was 'income(19.0%)', 'qualifications for job(18.0%)', 'path to job(18.0%)'. With survey analysis and expert consultation, 'Nurse Dream' was designed to have various categories including 'Nurse Career Guidance', 'Nurse Recruitment'. Developing portal web contents suggests that it is possible for nursing students to get enough information for career search and enhance career satisfaction, which is likely to give them positive impact.

• Journal of Plant Biotechnology
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• v.47 no.3
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• pp.218-226
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• 2020

This report describes methods for selecting informative single nucleotide polymorphisms (SNPs), and the development of an online Solanaceae genome database, using 234 tomato resequencing data entries deposited in the NCBI SRA database. The 126 accessions of Solanum lycopersicum, 68 accessions of Solanum lycopersicum var. cerasiforme, and 33 accessions of Solanum pimpinellifolium, which are frequently used for breeding, and some wild-species tomato accessions were included in the analysis. To select tag-SNPs, we identified 29,504,960 SNPs in 234 tomatoes and then separated the SNPs in the genic and intergenic regions according to gene annotation. All tag-SNP were selected from non-synonymous SNPs among the SNPs present in the gene region and, as a result, we obtained tag-SNP from 13,845 genes. When there were no non-synonymous SNPs in the gene, the genes were selected from synonymous SNPs. The total number of tag-SNPs selected was 27,539. To increase the usefulness of the information, a Solanaceae genome database website, TGsol (http://tgsol. seeders.co.kr/), was constructed to allow users to search for detailed information on resources, SNPs, haplotype, and tag-SNPs. The user can search the tag-SNP and flanking sequences for each gene by searching for a gene name or gene position through the genome browser. This website can be used to efficiently search for genes related to traits or to develop molecular markers.