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

Published: 07 June 2005 Publication History

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

[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]
X. Liu, J. Bollen, M. Nelson, H. Van de Sompel, J. Hussell, R. Luce, and L. Marks. Toolkits for Visualizing Co-Authorship Graph. Proceedings of the Fourth ACM/IEEE Joint Conference on Digital Libraries, page 404, 2004.
[4]
S. van Dongen. Markov Cluster Algorithm, 2000. http://micans.org/mcl/.

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  • (2018)System of Information Systems and Organizational Memory2018 13th Annual Conference on System of Systems Engineering (SoSE)10.1109/SYSOSE.2018.8428784(477-484)Online publication date: Jun-2018

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Published In

cover image ACM Conferences
JCDL '05: Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
June 2005
450 pages
ISBN:1581138768
DOI:10.1145/1065385
  • General Chair:
  • Mary Marlino,
  • Program Chairs:
  • Tamara Sumner,
  • Frank Shipman
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 June 2005

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Author Tags

  1. co-retrieval
  2. collection development
  3. data mining
  4. usage analysis

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JCDL05

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Overall Acceptance Rate 415 of 1,482 submissions, 28%

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Cited By

View all
  • (2018)System of Information Systems and Organizational Memory2018 13th Annual Conference on System of Systems Engineering (SoSE)10.1109/SYSOSE.2018.8428784(477-484)Online publication date: Jun-2018

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