TLabel: A New OLAP Aggregation Operator in Text Cubes

Lamia Oukid, Omar Boussaid, Nadjia Benblidia and Fadila Bentayeb
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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

International Journal of Data Warehousing and Mining (IJDWM), 2016, vol. 12, issue 4, 54-74

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.

Date: 2016
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