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Textual aggregation approaches in OLAP context: A survey

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  • Bouakkaz, Mustapha
  • Ouinten, Youcef
  • Loudcher, Sabine
  • Strekalova, Yulia

Abstract

In the last decade, OnLine Analytical Processing (OLAP) has taken an increasingly important role as a research field. Solutions, techniques and tools have been provided for both databases and data warehouses to focus mainly on numerical data. however these solutions are not suitable for textual data. Therefore recently, there has been a huge need for new tools and approaches that treat and manipulate textual data and aggregate it as well. Textual aggregation techniques emerge as a key tool to perform textual data analysis in OLAP for decision support systems. This paper aims at providing a structured and comprehensive overview of the literature in the field of OLAP Textual Aggregation. We provide a new classification framework in which the existing textual aggregation approaches are grouped into two main classes, namely approaches based on cube structure and approaches based on text mining. We discuss and synthesize also the potential of textual similarity metrics, and we provide a recent classification of them.

Suggested Citation

  • Bouakkaz, Mustapha & Ouinten, Youcef & Loudcher, Sabine & Strekalova, Yulia, 2017. "Textual aggregation approaches in OLAP context: A survey," International Journal of Information Management, Elsevier, vol. 37(6), pages 684-692.
    • Handle: RePEc:eee:ininma:v:37:y:2017:i:6:p:684-692
    DOI: 10.1016/j.ijinfomgt.2017.06.005
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    References listed on IDEAS

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    1. Josiane Mothe & Claude Chrisment & Bernard Dousset & Joel Alaux, 2003. "DocCube: Multi‐dimensional visualisation and exploration of large document sets," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 54(7), pages 650-659, May.
    2. Lamia Oukid & Nadjia Benblidia & Fadila Bentayeb & Ounas Asfari & Omar Boussaid, 2015. "Contextualized Text OLAP Based on Information Retrieval," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 11(2), pages 1-21, April.
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    Cited by:

    1. 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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