Abstract
This chapter offers a model for automated library material utilization that is based on knowledge discovery using association rules. Processing the circulation data of the library to extract the statistics and association utilization of the materials for departments is a great achievement that makes the analysis easier for calculating material utilization. Moreover, processing the circulation data of the library, two important dimensions, namely concentration and connection (Kao, Chang, & Lin, 2003), could be explored among departments and library members. This can make the analysis easier by calculating weights in those two important dimensions to make the decision about budget allocation. This chapter analyses the circulation data of North South University Library and suggests that efficient management and budget allocation can be achieved by using the above-mentioned metrics.
TopMany research works have been involved into budget allocation for library materials acquisition. Most of them are in common style and with a common objective, some have slightly different in objectives. We report findings from some of the researches related to our work below.
Kao, Chang, & Lin (2003) worked with the circulation database of Kun Shan University of Technology where they used ID3 (Quinlan,1986) algorithm of Data Mining. Wu, Lee, & Kao (2004) used circulation statistics mechanism and an association rule was applied to discover knowledge. There are several models approached by several researchers like ABAMDM (Kao, Chang, & Lin, 2003), KDBMLMA (Wu, Lee, & Kao, 2004) to help derive the utilization of library material categories. Although many approaches and research reports have been extensively used to help library material acquisitions, the knowledge contained in circulation databases has rarely been used to investigate in-depth how the acquired materials are being used. Wu & Lee (2005) presented a decision support model for library material acquisition and budget allocation using the knowledge derived from circulation databases.
Key Terms in this Chapter
Knowledge Discovery: Explore data to find some useful information.
Budget Allocation: Try to allocate limited budget to satisfy most of the users’ needs.
Data Mining: Method to reveal hidden information from data.
Decision Tree: A tree like structure that is used for classification of different records.
SQL-Structured Query Language: A special language to retrieve data from a computerized database.
Circulation Data: Loan information about book, CD etc. that are borrowed by users of library.