To read the full version of this content please select one of the options below:

Mining library and university data to understand library use patterns

John Renaud (UCI Libraries, University of California, Irvine, California, USA)
Scott Britton (University Libraries, Boston College, Chestnut Hill, Massachusetts, USA)
Dingding Wang (Center for Computational Science, University of Miami, Coral Gables, Florida, USA)
Mitsunori Ogihara (Department of Computer Science, University of Miami, Coral Gables, Florida, USA)

The Electronic Library

ISSN: 0264-0473

Article publication date: 1 June 2015



Library data are often hard to analyze because these data come from unconnected sources, and the data sets can be very large. Furthermore, the desire to protect user privacy has prevented the retention of data that could be used to correlate library data to non-library data. The research team used data mining to determine library use patterns and to determine whether library use correlated to students’ grade point average.


A research team collected and analyzed data from the libraries, registrar and human resources. All data sets were uploaded into a single, secure data warehouse, allowing them to be analyzed and correlated.


The analysis revealed patterns of library use by academic department, patterns of book use over 20 years and correlations between library use and grade point average.

Research limitations/implications

Analysis of more narrowly defined user populations and collections will help develop targeted outreach efforts and manage the print collections. The data used are from one university; therefore, similar research is needed at other institutions to determine whether these findings are generalizable.

Practical implications

The unexpected use of the central library by those affiliated with law resulted in cross-education of law and central library staff. Management of the print collections and user outreach efforts will reflect more nuanced selection of subject areas and departments.


A model is suggested for campus partnerships that enables data mining of sensitive library and campus information.



The authors would like to acknowledge the hard work and assistance provided by Christopher Mader, Luz Maristany and Joel Zysman at the Center for Computational Science, University of Miami.


Renaud, J., Britton, S., Wang, D. and Ogihara, M. (2015), "Mining library and university data to understand library use patterns", The Electronic Library, Vol. 33 No. 3, pp. 355-372.



Emerald Group Publishing Limited

Copyright © 2015, Emerald Group Publishing Limited