[논문]Utilization of Log Data Reflecting User Information-Seeking Behavior in the Digital Library

Lee, Seonhee; Lee, Jee Yeon

doi:10.1633/jistap.2022.10.1.6

Utilization of Log Data Reflecting User Information-Seeking Behavior in the Digital Library 원문보기

Journal of information science theory and practice : JISTaP, v.10 no.1, 2022년, pp.73 - 88

Lee, Seonhee (Korea Institute of Science and Technology Information) , Lee, Jee Yeon (Department of Library and Information Science, Yonsei University)

Abstract ▼ AI-Helper

This exploratory study aims to understand the potential of log data analysis and expand its utilization in user research methods. Transaction log data are records of electronic interactions that have occurred between users and web services, reflecting information-seeking behavior in the context of digital libraries where users interact with the service system during the search for information. Two ways were used to analyze South Korea's National Digital Science Library (NDSL) log data for three days, including 150,000 data: a log pattern analysis, and log context analysis using statistics. First, a pattern-based analysis examined the general paths of usage by logged and unlogged users. The correlation between paths was analyzed through a χ2 analysis. The subsequent log context analysis assessed 30 identified users' data using basic statistics and visualized the individual user information-seeking behavior while accessing NDSL. The visualization shows included 30 diverse paths for 30 cases. Log analysis provided insight into general and individual user information-seeking behavior. The results of log analysis can enhance the understanding of user actions. Therefore, it can be utilized as the basic data to improve the design of services and systems in the digital library to meet users' needs.

주제어

표/그림 (11)

표 Table 1. Research procedure and details
표 Table 2. Log master table schema
표 Table 3. Frequency of log data usage: service-main-class and service-sub-class (unit: case, %)
표 Table 4. Frequency of log data usage: content-main-class and content-sub-class (unit: case, %)
표 Table 5. X² test: frequency of content-main-class in the service-main-class
표 Table 6. X² test: frequency of content-sub-class in the service-sub-class
표 Table 7. Frequency analysis on subjects and preferred information types for 30 users
그림 Fig. 1. Visualization diagram of log context analysis: example of User 27.
표 Table 8. Visualization cross table for numbers of service and content pairs
그림 Fig. 2. Visualization diagram of context analysis: 30 users.
그림 Fig. 2. Continued.

AI 본문요약
AI-Helper

문제 정의

The study was divided into a log pattern analysis that focuses on transaction log analyses alone and a log context analysis that combines transaction log data with user informa- tion. The research revealed significant insights into the information-seeking behavior of domestic researchers in the digital library. Furthermore, the data pertaining to identified individuals can be analyzed from various angles through log analysis.
Furthermore, the data pertaining to identified individuals can be analyzed from various angles through log analysis. This study aims to understand the potential of log data and increase its utilization by using log data as a user research method.

제안 방법

This exploratory research was conducted to understand user information-seeking behavior by analyzing the log data left on the National Digital Science Library (NDSL, the previous service of ScienceON). The study was divided into a log pattern analysis that focuses on transaction log analyses alone and a log context analysis that combines transaction log data with user informa- tion. The research revealed significant insights into the information-seeking behavior of domestic researchers in the digital library.
This paper provided detailed records and descriptions of the information behavior of individuals who use the NDSL services by analyzing and visualizing user information behaviors that appear in the log data. Three components of duration (time), service, and content were visualized two-dimensionally.
This study visualized user information-seeking behavior using log data, since log data was an electronic code and showed the user information-seeking behavior that the user interacted with while accessing NDSL. From the perspective of log analysis, log context analysis included user information and log pattern analysis.

대상 데이터

Table 2 shows information about the field of the log master table schema of the NDSL. About 150, 000 pageviews of the randomly selected three-day NDSL logs in 2017 and 2018 were extracted and analyzed. There were 49, 121 page views (32.

이론/모형

Fur- thermore, the model should be structured to play a role in showing the dynamics of the information-seeking process according to the users’ situations and contexts. The model used in this study was derived inductively through actual log analysis. Visualization by model schematic diagram can be used to understand individuals’ distinctive use patterns by making it easy to recognize the information seeking behavior of NDSL users.

후속연구

The visualization tool is based on Excel; it can be used in similar cases that analyze log data. If user demands and usage patterns are researched through a highly usable log analysis method, it will be possible to make significant contributions to establishing a way to improve and design systems and services. Fur- thermore, the model should be structured to play a role in showing the dynamics of the information-seeking process according to the users’ situations and contexts.
Moreover, the proportion of users who log in may be smaller than that of university libraries or research institutions. Since information behaviors may differ depending on the structure of the digital information service, any attempts to apply the model to other digital library services must be preceded by a thorough review.
However, the information-seeking behavior of log-in users shown in the log context analysis also revealed the characteristics of a small number of information services that could be overlooked in the overall log data analysis. Therefore, it is desirable to perform a complimentary analysis of the entire log data and the individual user data can be considered in a complementary role as it is possible to analyze the use of NDSL from different angles. There- fore, the results of log context analysis results can be used to assess information behaviors for individuals to provide personalized services that cater to the individual needs of users.

참고문헌 (22)

Agosti, M., Crivellari, F., & Di Nunzio, G. M. (2012). Web log analysis: A review of a decade of studies about information acquisition, inspection and interpretation of user interaction. Data Mining and Knowledge Discovery, 24(3), 663-696. https://doi.org/10.1007/s10618-011-0228-8.

상세보기
Bates, M. J. (1989). The design of browsing and berrypicking techniques for the online search interface. Online Review, 13(5), 407-424. https://doi.org/10.1108/eb024320.

상세보기
Bollen, J., & Luce, R. (2002). Evaluation of digital library impact and user communities by analysis of usage patterns. http://www.dlib.org/dlib/june02/bollen/06bollen.html.
Borgman, C. L. (1986). The user's mental model of an information retrieval system: An experiment on a prototype online catalog. International Journal of Man-Machine Studies, 24(1), 47-64. https://doi.org/10.1016/S0020-7373(86)80039-6.

상세보기
Borgman, C. L., Hirsh, S. G., & Hiller, J. (1996). Rethinking online monitoring methods for information retrieval systems: From search product to search process. Journal of the American Society for Information Science, 47(7), 568-583. https://www.proquest.com/openview/20c67ac9d22a200ad3f6af80a29769c6/1?pq-origsitegscholar&cbl41136.

상세보기
Choi, D. W., Gang, J. Y., Yang, D., Lee, H., & Oh, H. J. (2018). An analysis of library culture program management based on users' participation logs: A case study of National Library of Korea, Sejong. Journal of Korean Library and Information Science Society, 49(1), 293-320. https://doi.org/10.16981/kliss.49.201803.293.

원문보기 상세보기
Choo, C. W., Detlor, B., & Turnbull, D. (2000). Information seeking on the web: An integrated model of browsing and searching. First Monday, 5(2). https://doi.org/10.5210/fm.v5i2.729.

상세보기
Jansen, B. J. (2006). Search log analysis: What it is, what's been done, how to do it. Library & Information Science Research, 28(3), 407-432. https://doi.org/10.1016/j.lisr.2006.06.005.

상세보기
Jansen, B. J., Spink, A., & Taksa, I. (2009). Handbook of research on web log analysis. IGI Global.
Jin, J. Y., & Rieh, H. (2018). Analysis of users' inflow route and search terms of the Korea National Archives' web site. Journal of the Korean Society for information Management, 35(1), 183-203. https://doi.org/10.3743/KOSIM.2018.35.1.183.

원문보기 상세보기
Kim, G. Y., Kwon, N. H., Yu, S. Y., & Choi, Y. (2013). An analysis of NDSL use statistics and its application for developing virtuous circle of NDSL information services. https://scienceon.kisti.re.kr/commons/util/originalView.do?cnTRKO201500002288&dbtTRKO&rn.
Koch, T., Ardo, A., & Golub, K. (2004, June 7-11). Browsing and searching behavior in the Renardus web service: A study based on log analysis. Paper presented at the 4th ACM/IEEE-CS Joint Conference on Digital Libraries, Tuscon, AZ, USA.
Lee, H., & Yim, J. H. (2015). A case study analysing the users of archives through web analytics. Korean Journal of Archival Studies, 45, 83-120. https://doi.org/10.20923/kjas.2015.45.083.

원문보기 상세보기
Lee, T. S., Jeong, D. H., Moon Y. S., Park, M. S., & Hyun, M. H. (2012). An analytic study on the categorization of query through automatic term classification. The KIPS Transactions: Part D, 19D(2), 133-138. https://doi.org/10.3745/KIPSTD.2012.19D.2.133.

원문보기 상세보기
Park, M., & Lee, T.-S. (2013). Understanding science and technology information users through transaction log analysis. Library Hi Tech, 31(1), 123-140. https://doi.org/10.1108/07378831311303976.

상세보기
Park, M., & Lee, T.-S. (2016). A longitudinal study of information needs and search behaviors in science and technology: A query analysis. Electronic Library, 34(1), 83-98. https://doi.org/10.1108/EL-04-2014-0058.

상세보기
Park, S. Y. (2011). Trends and changes of web searching behavior. Journal of the Korean Society for Library and Information Science, 45(1), 377-393. https://doi.org/10.4275/KSLIS.2011.45.1.377.

원문보기 상세보기
Park, S. Y., & Lee, J. H. (2007). Applications of transaction log analysis for the web searching field. Journal of the Korean Society for Library and Information Science, 41(1), 231-242. https://doi.org/10.4275/KSLIS.2007.41.1.231.

원문보기 상세보기
Penniman, W. D., & Dominick, W. D. (1980). Monitoring and evaluation of on-line information system usage. Information Processing & Management, 16(1), 17-35. https://doi.org/10.1016/0306-4573(80)90003-5.

상세보기
Peters, T. A. (1993). The history and development of transaction log analysis. Library Hi Tech, 11(2), 41-66. https://doi.org/10.1108/eb047884.

상세보기
Rice, R. E., & Borgman, C. L. (1983). The use of computer-monitored data in information science and communication research. Journal of the American Society for Information Science, 34(4), 247-256. https://www.dhi.ac.uk/san/waysofbeing/data/health-jones-rice-1983c.pdf.

상세보기
Yoo, S.-R. (2002). User-oriented evaluation of NDSL information service. Journal of the Korean Society for Library and Information Science, 36(1), 25-40. https://doi.org/10.4275/KSLIS.2002.36.1.025.

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표제어: PCR

동의어: Packet Collision Rate

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용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

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