A case study of library data management: A new method to analyze borrowing behavior

Abstract

The library data management system of academic institutions generates and stores data with information on lends, returns and queries made by users daily. This research aims to provide a useful method to transform this data to obtain relevant knowledge for the management of the library. The process used consisted of a data pre-processing, data transformation and cleaning, and finally the use of privacy and association algorithms. We clarify the proposed method for a university library in Peru where significant results were obtained in the form of association rules. This shows that it is a efficient alternative to have a more detailed knowledge to make better decisions.