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OLAP and bibliographic databases

Author

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  • Emil Hudomalj

    (Faculty of Medicine University of Ljubljana)

  • Gaj Vidmar

    (Faculty of Medicine University of Ljubljana)

Abstract

The application of online analytical processing (OLAP) technology to bibliographic databases is addressed. We show that OLAP tools can be used by librarians for periodic and ad hoc reporting, quality assurance, and data integrity checking, as well as by research policy makers for monitoring the development of science and evaluating or comparing disciplines, fields or research groups. It is argued that traditional relational database management systems, used mainly for day-to-day data storage and transactional processing, are not appropriate for performing such tasks on a regular basis. For the purpose, a fully functional OLAP solution has been implemented on Biomedicina Slovenica, a Slovenian national bibliographic database. We demonstrate the system's usefulness by extracting data for studying a selection of scientometric issues: changes in the number of published papers, citations and pure citations over time, their dependence on the number of co-operating authors and on the number of organisations the authors are affiliated to, and time-patterns of citations. Hardware, software and feasibility considerations are discussed and the phases of the process of developing bibliographic OLAP applications are outlined.

Suggested Citation

  • Emil Hudomalj & Gaj Vidmar, 2003. "OLAP and bibliographic databases," Scientometrics, Springer;Akadémiai Kiadó, vol. 58(3), pages 609-622, November.
  • Handle: RePEc:spr:scient:v:58:y:2003:i:3:d:10.1023_b:scie.0000006883.28709.d2
    DOI: 10.1023/B:SCIE.0000006883.28709.d2
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    References listed on IDEAS

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    1. Leo Egghe & Ronald Rousseau, 2000. "Aging, obsolescence, impact, growth, and utilization: Definitions and relations," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 51(11), pages 1004-1017.
    2. Quentin L. Burrell, 2002. "Modelling citation age data: Simple graphical methods from reliability theory," Scientometrics, Springer;Akadémiai Kiadó, vol. 55(2), pages 273-285, August.
    3. Michael D. Gordon & Robert K. Lindsay, 1996. "Toward discovery support systems: A replication, re‐examination, and extension of Swanson's work on literature‐based discovery of a connection between Raynaud's and fish oil," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 47(2), pages 116-128, February.
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    Cited by:

    1. Alfio Ferrara & Silvia Salini, 2012. "Ten challenges in modeling bibliographic data for bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(3), pages 765-785, December.
    2. Sabine Loudcher & Wararat Jakawat & Edmundo Pavel Soriano Morales & Cécile Favre, 2015. "Combining OLAP and information networks for bibliographic data analysis: a survey," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 471-487, May.
    3. Yu, Hairong & Davis, Mari & Wilson, Concepción S. & Cole, Fletcher T.H., 2008. "Object-relational data modelling for informetric databases," Journal of Informetrics, Elsevier, vol. 2(3), pages 240-251.

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