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Object-relational data modelling for informetric databases

Author

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  • Yu, Hairong
  • Davis, Mari
  • Wilson, Concepción S.
  • Cole, Fletcher T.H.

Abstract

Informetric researchers have long chafed at the limitations of bibliographic databases for their analyses, without being able to visualize or develop real solutions to the problem. This paper describes a solution developed to provide for the specialist needs of informetric researchers. In a collaborative exercise between the fields of computer science and informetrics, data modelling was used in order to address the requirements of complex and dynamic informetric data. This paper reports on this modelling experience with its aim of building an object-relational database (ORDB) for informetric research purposes. The paper argues that ORM (object-relational model) is particularly suitable because it allows for the modelling of complex data and accommodates the various data source formats and standards used by a variety of bibliographic databases. Further, ORM captures the dynamic nature of informetric data by allowing user-defined data types and by embedding basic statistical calculating tools as object functions in these user-defined data types. The main ideas of the paper are implemented in an Oracle database management system.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:infome:v:2:y:2008:i:3:p:240-251
    DOI: 10.1016/j.joi.2008.06.001
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    References listed on IDEAS

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    1. William W. Hood & Concepción S. Wilson, 2003. "Informetric studies using databases: Opportunities and challenges," Scientometrics, Springer;Akadémiai Kiadó, vol. 58(3), pages 587-608, November.
    2. Emil Hudomalj & Gaj Vidmar, 2003. "OLAP and bibliographic databases," Scientometrics, Springer;Akadémiai Kiadó, vol. 58(3), pages 609-622, November.
    3. Kreps, David M. & Wilson, Robert, 1982. "Reputation and imperfect information," Journal of Economic Theory, Elsevier, vol. 27(2), pages 253-279, August.
    4. anonymous, 1999. "Informing customers about the Year 2000," Financial Update, Federal Reserve Bank of Atlanta, vol. 12(Apr), pages 1-7.
    5. Mari Davis & Concepción S. Wilson, 2001. "Elite Researchers in Ophthalmology: Aspects of Publishing Strategies, Collaboration and Multi-Disciplinarity," Scientometrics, Springer;Akadémiai Kiadó, vol. 52(3), pages 395-410, November.
    6. Timo Niemi & Lasse Hirvonen & Kalervo Järvelin, 2003. "Multidimensional Data Model and Query Language for Informetrics," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 54(10), pages 939-951, August.
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    Cited by:

    1. Mallig, Nicolai, 2010. "A relational database for bibliometric analysis," Journal of Informetrics, Elsevier, vol. 4(4), pages 564-580.
    2. 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.
    3. Mallig, Nicolai, 2010. "A relational database for bibliometric analysis," Discussion Papers "Innovation Systems and Policy Analysis" 22, Fraunhofer Institute for Systems and Innovation Research (ISI).
    4. Gagolewski, Marek, 2011. "Bibliometric impact assessment with R and the CITAN package," Journal of Informetrics, Elsevier, vol. 5(4), pages 678-692.

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