Published: October 3, 2019 by Dr. Geoffrey Liu

With operation being increasingly computerized and services becoming interactive online, libraries – like other business organizations – are accumulating huge piles of data. Such data include not only operational/circulation records and online transactions on web platform, but also textual information generated by library virtual communities and data collected through service programs. In this sense, the “Big Data” movement did not leave libraries out.

However, in spite of much talking about “big data” in libraries and the call for “mining library data” appearing in 1990s (Banerjee, 1998; Mancini, 1996; Peters, 1996) and growing stronger later (Guenther, 2000; Collier, 2003; Dhiman, 2003; Papatheodorou, Kapidakis, Sfakakis, & Vassiliou, 2003; Prakash, Chand, & Gohel, 2004; Lavoie,  Dempsey, & Connaway, 2006; Chiang, 2010; Shieh, 2010; etc.), serious projects of mining library data are scarce, and most published efforts were limited to mining web transaction logs (Banks, 2000; Blecic et al., 1998; Jones, Cunningham, McNab, & Boddie, 2000; Peters, 1996; Yan, Zhang, Tang, Sun, Deng, & Xiao, 2010; Warren, 2002;). Only a few researchers went beyond mining transaction logs, and the work by Wu’s team (Wu, 2003; Wu, Lee, & Kao, 2004) and Nicholson’s (2003a; 2003b; 2006) focus on “biblioming” are particularly worthy to note here.

The possibilities of applying data mining in library settings and information services are abundant, given the richness of data accumulated therein. Besides mining web transaction logs for understanding usage trends and circulation transactions for investigating readership trends (Yu, 2011), service/program-specific data (such as records of online reference services) may be mined for managerial decision making, strategic planning, resource allocation, and optimization of operational processes (e.g. Kao, Chang, & Lin, 2003). Furthermore, data models of prediction can be trained with records of technical processing to develop computer-aided workflows for improved effectiveness and efficiency, as demonstrated by Wagstaff and Liu’s (2018) work on improving the collection-weeding process.

But all of the possibilities hinge on libraries’ willingness to invest in in-house data mining or to collaborate and share their data with researchers.

References

Banerjee, K. (1998). Is data mining right for your library? Computers in Libraries, 18(10), 28-31.

Banks, J. (2000). Are transaction logs useful? A ten-year study. Journal of Southern Academic and Special Librarianship, 1(3).

Blecic, D.D., Bangalore, N.S., Dorsch, J.L., Henderson, C.L., Keonig, M.H., & Weller, A.C. (1998). Using transaction log analysis to improve OPAC retrieval results. College & Research Libraries, Jan. 1998, 39-50.

Chiang, K. (2010). Data mining, data fusion, and libraries. The 31st Annual IATUL Conference (International Association of Scientific and Technological University Libraries).

Collier, H. (2003). Data mining: Does it have applications within the world of libraries? Series: The Journal for the Series Community, 16(2), 209-210.

Dhiman, A.K. (2003). Data mining and its use in libraries. CALIBER 2003: Ahmedabad. 

Guenther, K. (2000). Applying data mining principles to library data collection. Computers in Libraries, 20(4), 60-63.

Jones, S., Cunningham, S.J., McNab, R. J., & Boddie, S. (2000). A transaction log analysis of a digital library. International Journal on Digital Libraries, 3(2),152-169.

Kao, S., Chang, H., & Lin, C. (2003). Decision support for the academic library acquisition budget allocation via circulation database mining. Information Processing &Management, 39(1), 133-148.

Lavoie, B., Dempsey, L., & Connaway, L.S. (2006, Jan. 15). Making data work harder. Library Journal e-Newsletters. LibraryJournal.com.

Mancini, D. D. (1996). Mining your automated system for systemwide decision making. Library Administration & Management, 10(1), 11-15.

Nicholson, S. (2003a). The bibliomining process: Data warehousing and data mining for library decision-making. Information Technology and Libraries, 22(4), 146-151.

Nicholson, S. (2003b). Bibliomining for automated collection development in a digital library setting: Using data mining to discover web-based scholarly research works. Journal of the American Society for Information Science and Technology, 54(12), 1081-1090.

Nicholson, S. (2006). The basis for bibliomining: Frameworks for bringing together usage-based data mining and bibliometrics through data warehousing in digital library services. Information Processing and Management, 42(3), 785-804.

Papatheodorou, C., Kapidakis, S. Sfakakis, M., and Vassiliou, A. (2003). Mining user communities in digital libraries, Information Technology and Libraries 22(4). 152-157.

Peters, T. (1996). Using transaction log analysis for library management information. Library Administration & Management, 10(1), 20-25.

Prakash, K., Chand, P., & Gohel, U. (2004). Application of data mining in library and information services. 2^nd Convention PLANNER-2004, Manipur Uni., Imphal, November 4-5, 2004. 

Shieh, J.C. (2010). The integration system for librarians’ bibliomining. The Electronic Library, 28(5), 709-721.

Warren, N. (2002). Website log analysis: Approaches for the library of the National Institute of Environmental Health Sciences. Master thesis. University of North Carolina at Chapel Hill: School of Information and Library Science.  Available from http://ils.unc.edu/MSpapers/2785.pdf 

Wagstaff, K., & Liu, G. (2018). Automated classification to improve the efficiency of weeding library collections. Journal of Academic Librarianship, 44(2), 238-247.

Wu, C.H. (2003). Data mining applied to material acquisition budget allocation for libraries: Design and development. Expert Systems with Applications, 25(3), 401-411.

Wu, C.H., Lee, T.Z., & Kao, S.C. (2004). Knowledge discovery applied to material acquisition for libraries. Information Processing & Management, 40(4), 709-725.

Yan, F. Zhang, M., Tang, J., Sun, T., Deng, Z., & Xiao, L. (2010). Users’ book-loan behaviors analysis and knowledge dependency mining. Web-Age Information Management: Lecture Notes in Computer Science, 6184, 206-217.

Yu, P. (2011). Data mining in library reader management. Proceedings of International Conference on Network Computing and Information Security (NCIS), (pp.54-57). IEEE Xplore Digital Library. doi: 10.1109/NCIS.2011.109