2018, Vol.35, No.1

Journal of the Korean Society for Information Management 2023 KCI Impact Factor : 1.31

pISSN : 1013-0799 / eISSN : 2586-2073
https://journal.kci.go.kr/kosim

The purpose of this study is to analyze the association among the subject areas of big data research papers. The subject group of the units of analysis was extracted by applying co-citation networks, and the rules of association were analyzed using Apriori algorithm of R program, and visualized using the arulesViz package of R program. As a result of the study, 22 subject areas were extracted and these subjects were divided into three clusters. As a result of analyzing the association type of the subject, it was classified into ‘professional type’, ‘general type’, ‘expanded type’ depending on the complexity of association. The professional type included library and information science and journalism. The general type included politics & diplomacy, trade, and tourism. The expanded types included other humanities, general social sciences, and general tourism. This association networks show a tendency to cite other subject areas that are relevant when citing a subject field, and the library should consider services that use the association for academic information services.

This study draws on the current momentum to diversify open government data research through multidimensional scaling and model development. It formulates a quality assessment model applicable to library open data, taking into consideration the paucity of such research in the field. The model was developed using the Delphi method and verified for validity and reliability on the basis of a survey administered to library open data users. The results of the fourth round exhibited an average of 4.00 for all measured elements and a minimum validity of .75, rendering the model appropriate for use in quality assessments of library open data. The convergence and stability results provided by the expert panel fell below .50, confirming that there was no need to conduct further surveys in order to establish the validity of the Delphi method. The model's reliability likewise garnered results of .60 and above in all three dimensions. This Model completed with the input of the Delphi panel was put through a verification process in which library open data users such as domestic and international librarians, developers, and open data activists reviewed the model for validity and reliability. The model scored low on validity on account of its failure to load all measure factors and elements pertaining to the three dimensions. Reliability results, on the other hand, were at 0.6 and above for all dimensions and measured elements.

Fiction is a collection that most students read and borrow in school libraries. KDC has several limitations when students look for fiction books they need. In line with this, we surveyed various cases of fiction classifications used in libraries, bookstores, and publishers and use behaviors of fiction of middle school students. Based upon the result of the surveys, we proposed a better way of classifying fiction books according to user needs. In addition to the KDC number, color bands were attached according to genres so that users could easily find the desired books. These suggestions and other information will enhance the accessibility and discoverability to fiction books for middle school students and may be used as reference materials for fiction classification in libraries, bookstores, and publishers in the future.