Mining and analyzing digital archive usage data to support collection development decisions

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Mining and analyzing digital archive usage data to support collection development decisions

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International Conference on Digital Libraries archive
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries table of contents

Denver, CO, USA

DEMONSTRATION SESSION: Demonstrations table of contents

Pages: 417 - 417

Year of Publication: 2005

ISBN:1-58113-876-8

Authors

Jewel Ward	University of Southern California, Los Angeles, CA
Johan Bollen	Old Dominion University, Norfolk, VA
Jeffrey Pearson	University of Southern California, Los Angeles, CA
Shing-Cheung Chan	University of Southern California, Los Angeles, CA
Hui-Hsien Chi	University of Southern California, Los Angeles, CA
Marie Chi	University of Southern California, Los Angeles, CA
Kristine Guevara	University of Southern California, Los Angeles, CA
Hsiao-han Huang	University of Southern California, Los Angeles, CA
Genesan Kim	University of Southern California, Los Angeles, CA
Maks Krivokon	University of Southern California, Los Angeles, CA
Bo H. Lee	University of Southern California, Los Angeles, CA
Pei-Han Li	University of Southern California, Los Angeles, CA
Fenny Muliawan	University of Southern California, Los Angeles, CA
Vu Nguyen	University of Southern California, Los Angeles, CA
Barry W. Boehm	University of Southern California, Los Angeles, CA
A. Winsor Brown	University of Southern California, Los Angeles, CA
Edward Colbert	University of Southern California, Los Angeles, CA
Alex Lam	University of Southern California, Los Angeles, CA
Mayur Patel	University of Southern California, Los Angeles, CA

Sponsors

ACM: Association for Computing Machinery
SIGIR: ACM Special Interest Group on Information Retrieval
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

ACM New York, NY, USA

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ABSTRACT

We demonstrate a "collection development decision support tool" that mines digital archive usage data. We want to better understand the University of Southern California (USC) Digital Archive's collection structure by analyzing the objects' characteristics, by analyzing the relationships between viewed objects, and by understanding usage trends over time. By relying on implicit patterns of usage data, such as co-retrievals, rather than explicit data, such as hit counts, we believe we can make more informed decisions about where to expend our resources.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	T. Munzner. H3 Viewer, 2002. http://graphics.stanford.edu/~munzner/h3/.
	2	J. Bollen and R. Luce. Evaluation of Digital Library Impact and User Communities by Analysis of Usage. D-Lib Magazine, 8(6), 2002.
	3	Xiaoming Liu , Johan Bollen , Michael L. Nelson , Herbert Van de Sompel , Jeremy Hussell , Rick Luce , Linn Marks, Toolkits for visualizing co-authorship graph, Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries, June 07-11, 2004, Tuscon, AZ, USA [doi>10.1145/996350.996470]
	4	S. van Dongen. Markov Cluster Algorithm, 2000. http://micans.org/mcl/.