Home | Browse | Search | Credits | About
Register | User Area | DL-Harvest | Help
DLIST

The Bibliomining Process: Data Warehousing and Data Mining for Library Decision-Making

Nicholson, Scott (2003) The Bibliomining Process: Data Warehousing and Data Mining for Library Decision-Making. Information Technology and Libraries 22(4):pp. 4-9.

Full text available as:
PDF - Requires Adobe Acrobat Reader or other PDF viewer.

Abstract

The goal of this brief article is to explain the bibliomining process. Emphasis is placed on data warehousing and patron privacy issues because they are required before anything else can begin. It is essential to capture our data-based institutional records while still protecting the privacy of users. By using a data warehouse, both goals can be met. Once the data warehouse is in place, the library can use reporting and exploration tools to gain a more thorough knowledge of their user communities and resource utilization.

EPrint Type:Journal Article (Paginated)
Keywords:Library user communities Evaluation techniques Data-based artifacts
Subjects:Libraries
Data Mining
ID Code:728
Deposited On:14 February 2005
Alternative Locations:http://bibliomining.com/nicholson/biblioprocess.htm, http://bibliomining.com/nicholson/nicholsonpdfs/biblioprocess.pdf
Eprint Statistics:View statistics for this eprint
Tell A Colleague:Tell a colleague about it.

Buckland, M. 2003. Five grand challenges for library research. Library Trends 51(4). [cited 27 June 2003]. Available from http://www.sims.berkeley.edu/~buckland/trends03.pdf.

Berry, M. and G. Linoff. 1997. Data Mining Techniques For Marketing, Sales, and Customer Support. New York: John Wiley & Sons.

Berry, M. and G. Linoff. 2000. Mastering Data Mining. New York: John Wiley & Sons.

Inmon, W. 2002. Building the Data Warehouse, 3rd Edition. New York: John Wiley & Sons.

Nicholson, S. and J. Stanton. 2003. “Gaining strategic advantage through bibliomining: Data mining for management decisions in corporate, special, digital, and traditional libraries.” In Organizational Data Mining: Leveraging Enterprise Data Resources for Optimal Performance, ed. H. Nemati & C. Barko. Hershey, PA: Idea Group Publishing.

Witten, I. and E. Frank. 1999. Practical Machine Learning Tools and Techniques with Java Implementations. San Francisco, CA: Morgan Kaufmann.

EPrints dLIST, an open access archive for the Information Sciences, is supported by the School of Information Resources and Library Science and Learning Technologies Center, University of Arizona. Established in 2002, dLIST has a global Advisory Board and is a part of the Information Technology & Society Research Lab. Open Archives
Contact: Admin | Donate