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TLabel: A New OLAP Aggregation Operator in Text Cubes

Author

Listed:
  • Lamia Oukid

    (LRDSI Laboratory, University of Blida 1, Blida, Algeria)

  • Omar Boussaid

    (ERIC Laboratory, University of Lyon 2, Lyon, France)

  • Nadjia Benblidia

    (LRDSI Laboratory, University of Blida 1, Blida, Algeria)

  • Fadila Bentayeb

    (ERIC Laboratory, University of Lyon 2, Lyon, France)

Abstract

Data Warehousing technologies and On-Line Analytical Processing (OLAP) feature a wide range of techniques for the analysis of structured data. However, these techniques are inadequate when it comes to analyzing textual data. Indeed, classical aggregation operators have earned their spurs in the online analysis of numerical data, but are unsuitable for the analysis of textual data. To alleviate this shortcoming, on-line analytical processing in text cubes requires new analysis operators adapted to textual data. In this paper, the authors propose a new aggregation operator named Text Label (TLabel), based on text categorization. Their operator aggregates textual data in several classes of documents. Each class is associated with a label that represents the semantic content of the textual data of the class. TLabel is founded on a tailoring of text mining techniques to OLAP. To validate their operator, the authors perform an experimental study and the preliminary results show the interest of their approach for Text OLAP.

Suggested Citation

  • Lamia Oukid & Omar Boussaid & Nadjia Benblidia & Fadila Bentayeb, 2016. "TLabel: A New OLAP Aggregation Operator in Text Cubes," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 12(4), pages 54-74, October.
    • Handle: RePEc:igg:jdwm00:v:12:y:2016:i:4:p:54-74
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