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Combining OLAP and information networks for bibliographic data analysis: a survey

Author

Listed:
  • Sabine Loudcher

    (Université de Lyon, (ERIC LYON 2))

  • Wararat Jakawat

    (Université de Lyon, (ERIC LYON 2))

  • Edmundo Pavel Soriano Morales

    (Université de Lyon, (ERIC LYON 2))

  • Cécile Favre

    (Université de Lyon, (ERIC LYON 2))

Abstract

In the context of scientometrics and bibliometrics, several research fields are dealing with bibliographic data. In this paper, we will explore how the combination of online analytical processing (OLAP) analysis and information networks could be an interesting issue. In Business Intelligence, OLAP is a technology supported by data warehousing systems. It provides tools for analyzing data according to multiple dimensions and multiple hierarchical levels. At the same time, several information networks (co-authors network, citations network, institutions network, etc.) can be built based on bibliographic databases. Originally, OLAP was introduced to analyze structured data. However, in this paper, we wonder if, by combining OLAP and information networks, we can provide a new way of analyzing bibliographic data. OLAP should be able to handle information networks and be also useful for monitoring, browsing and analyzing the content and the structure of bibliographic networks. The goal of this survey paper is to review previous work on OLAP and information networks dealing with bibliographic data. We also propose a comparison between traditional OLAP and OLAP on information networks and discuss the challenges OLAP faces regarding bibliographic networks.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:103:y:2015:i:2:d:10.1007_s11192-015-1539-0
    DOI: 10.1007/s11192-015-1539-0
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    Cited by:

    1. Lee, O-Joun & Jeon, Hyeon-Ju & Jung, Jason J., 2021. "Learning multi-resolution representations of research patterns in bibliographic networks," Journal of Informetrics, Elsevier, vol. 15(1).
    2. Amine Ghrab & Oscar Romero & Sabri Skhiri & Esteban Zimányi, 2021. "TopoGraph: an End-To-End Framework to Build and Analyze Graph Cubes," Information Systems Frontiers, Springer, vol. 23(1), pages 203-226, February.
    3. Jérôme Darmont & Boris Novikov & Robert Wrembel & Ladjel Bellatreche, 2022. "Advances on Data Management and Information Systems," Information Systems Frontiers, Springer, vol. 24(1), pages 1-10, February.

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