Library and University Data Analysis Using Data Detection

Abstract

Library data are always hard to analyze because the datasets may be very large, and these data come from unconnected sources. This paper used data detection to identify academic library use patterns and to judge whether students' grade point average correlated to academic library use. All datasets of this paper were uploaded into a secure, single data warehouse, allowing them to be managed, supervised and analyzed. The detection showed patterns of library use by academic department, patterns of book use 15 years and statistics between grade point average and academic library use. The technique is demonstrated using data collected at Hohai University in China. In this paper, a model is advised for campus partnerships that empowers data detection of campus information and sensitive library.