• Journal of the Korean Society for information Management
• /
• v.37 no.3
• /
• pp.25-50
• /
• 2020

Since big data platform services for the public library began January 1, 2016, libraries have used big data to improve their work performance. This paper aims to examine the use cases of library big data and attempts to draw improvement plan to improve the effectiveness of library big data. For this purpose, first, we examine big data used while utilizing the library big data platform, the usage pattern of big data and services/policies drawn by big data analysis. Next, the limitations and advantages of the library big data platform are examined by comparing the data analysis of the integrated library management system (ILUS) currently used in public libraries and data analysis through the library big data platform. As a result of case analysis, big data usage patterns were found program planning and execution, collection, collection, and other types, and services/policies were summarized as customizing bookshelf themes for the book curation and reading promotion program, increasing collection utilization, and building a collection based on special topics. and disclosure of loan status data. As a result of the comparative analysis, ILUS is specialized in statistical analysis of library collection unit, and the big data platform enables selective and flexible analysis according to various attributes (age, gender, region, time of loan, etc.) reducing analysis time. Finally, the limitations revealed in case analysis and comparative analysis are summarized and suggestions for improvement are presented.

• Journal of Digital Convergence
• /
• v.20 no.3
• /
• pp.41-52
• /
• 2022

Although domestic public libraries achieved quantitative growth based on the 1st and 2nd comprehensive library development plans, there were some qualitative shortcomings, and various studies have been conducted to improve them. Most of the preceding studies have limitations in that they are limited to social and economic factors and statistical analysis. Therefore, in this study, by applying the spatiotemporal concept to quantitatively calculate the decrease in public library loan demand due to rainfall and heatwave, by clustering areas with high demand for book loan due to weather changes and areas where it is not, factors inside and outside public libraries and After the combination, changes in public library loan demand according to weather changes were analyzed. As a result of the analysis, there was a difference in the decrease due to the weather for each public library, and it was found that there were some differences depending on the characteristics and spatial location of the public library. Also, when the temperature was over 35℃, the decrease in book loan demand increased significantly. As internal factors, the number of seats, the number of books, and area were derived. As external factors, the public library access ramp, cafe, reading room, floating population in their teens, and floating population of women in their 30s/40s were analyzed as important variables. The results of this analysis are judged to contribute to the establishment of policies to promote the use of public libraries in consideration of the weather in a specific season, and also suggested limitations of the study.

• Journal of Korean Library and Information Science Society
• /
• v.52 no.3
• /
• pp.287-311
• /
• 2021

It is not easy for information users to find books that are suitable for them in a knowledge information society. There is a growing need for libraries to break away from traditional services and provide user-tailored recommendation services, but there are few qualitative studies on user satisfaction so far. In this study, a user-customized book recommendation was performed by applying Apriori, a correlation analysis algorithm, and satisfaction factors were analyzed in depth through interviews. The experimental data was the loan data of 100 people who used the most frequently used loan data for 10 years from 2009 to 2019 of the S library in Seoul. The interviewees of the experiment were those who could be interviewed in depth. After the correlation analysis, the concepts and categories derived by analyzing the interview data were 59 concepts, 6 sub-categories, and 2 upper categories, respectively. The upper categories were 'reading' and 'book recommendation service'. In the 'reading' category, there were 16 concepts of motivation for reading, 8 concepts of preferred books, and 12 concepts of expected effects. Also, in the category of 'reading recommendation service', there were 10 'reflection factors', 4 'reflection methods', and 9 'satisfaction factors'.

• Journal of Digital Convergence
• /
• v.11 no.2
• /
• pp.175-182
• /
• 2013

In this paper, the simulation model of house price is presented on the basis of pricing mechanism between the demand and the supply of apartments in seoul. The algorithm of house price simulation model for calculating the rate of price over time includes feedback control theory. The feedback control theory consists of stock variable, flow variable, auxiliary variable and constant variable. We suggest that the future price of apartment is simulated using mutual interaction variables which are demand, supply, price and parameters among them. In this paper we considers three items which include the behavior of apartment price index, the size of demand and supply, and the forecasting of the apartment price in the future economic scenarios. The proposed price simulation model could be used in public needs for developing a house price regulation policy using financial and non-financial aids. And the quantitative simulation model is to be applied in practice with more specific real data and Powersim Software modeling tool.

• Journal of the Korean Data and Information Science Society
• /
• v.26 no.6
• /
• pp.1417-1426
• /
• 2015

We develop the high risk group predictive model for loan default by utilizing the direct loan data from 2012 to 2014 of the Korea Student Aid Foundation. We perform the decision tree analysis using the data mining methodology and use SAS Enterprise Miner 13.2. As a result of this model, subject types were classified into 25 types. This study shows that the major influencing factors for the loan default are household income, national grant, age, overdue record, level of schooling, field of study, monthly repayment. The high risk group predictive model in this study will be the basis for segmented management service for preventing loan default.

• Journal of the Korean Society for Library and Information Science
• /
• v.58 no.2
• /
• pp.101-120
• /
• 2024

As the number of smart libraries is continuously increasing, activation measures are necessary to expand user engagement and enhance service quality. This study analyzed the usage patterns of four smart libraries in A Library in Incheon, from June 2020 to December 2023. It also conducted user satisfaction surveys and question-and-answer sessions with a librarian. The number of books and loans increased, and two smart libraries in areas without public libraries showed high usage rates. Although users were satisfied with the smart libraries, they pointed out inconveniences such as the limited number of loan books and insufficient books. Consequently, it is suggested to improve user-centered service quality, continuous monitoring and evaluation, as well as active promotion.

• Journal of Korean Library and Information Science Society
• /
• v.44 no.4
• /
• pp.399-419
• /
• 2013

This paper conducted the empirical analysis of the behaviors revealed in the eight size distributions of the public libraries in Korea. As a result, the behaviors of complex system appeared in all eight size factors. This means that the sizes of public libraries in Korea were highly polarized. Especially, the zipf's law were found in the size factors such as gross area, number of staffs, volume of books, total budget. And the highly uneven distributions were occurred in the size factors such as membership, number of users, number of borrowers, number of borrowed books. This research outcomes show that a new policy of public libraries is needed to resolve the polarization revealed in the sizes of public libraries in Korea.

• Journal of Information Management
• /
• v.28 no.1
• /
• pp.62-89
• /
• 1997

The purpose of this study is to propose the efficient and reasonable weeding model of the scientific literature by measuring a life-span of literatures in the field of nuclear engineering through citation analysis, the average life of actually discarded literatures in the KAERI (Korea Atomic Energy Research Institute), and the life of use through the circulation data analysis from 1992 to 1994 of KAERI.

• Journal of the Korean Society for Library and Information Science
• /
• v.44 no.4
• /
• pp.457-477
• /
• 2010

Currently, collections with public libraries are evaluated only on the basis of simple data, such as the use volume of collections with individual libraries, and interlibrary lending (ILL) results. To promote the ILL of regional libraries and raise the use of collections, however, the evaluation of collections should be expanded from individual libraries to regional libraries. This study aims to propose user-oriented methods for evaluating the library collections within a regional library system by using four kinds of data: collection, acquisition, circulation, and ILL application data. The results of this study show that the proposed method can reveal the positions of each library within a regional library system, and also the characteristics of a library's collections and users' needs more precisely.

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.5
• /
• pp.1293-1305
• /
• 2016

In this paper, we develop debt collection predictive models for the person in arrears by utilizing the direct loan data of the Korea Student Aid Foundation. We suggest credit risk scorecards for overdue student direct loan using the developed 3 models. Model 1 is designed for 1 month overdue, Model 2 is designed for 2 months overdue, and Model 3 is designed for overdue over 2 months. Model 1 shows that the major influencing factors for the delinquency are overdue account, due data for payment, balance, household income. Model 2 shows that the major influencing factors for delinquency loan are days in arrears, balance, due date for payment, arrears. Model 3 shows that the major influencing factors for delinquency are the number of overdue in recent 3 months, due data for payment, overdue account, arrears. The debt collection predictive models and credit risk scorecards in this study will be the basis for segmented management service and the call & collection strategies for preventing delinquency.