CXT-cube: contextual text cube model and aggregation operator for text OLAP

Authors:
Lamia Oukid;Ounas Asfari;Fadila Bentayeb;Nadjia Benblidia;Omar Boussaid
Affiliations:
LRDSI Laboratory, University of Blida, Saad Dahlab, Blida, Algeria;University of Lyon, ERIC, Lyon 2, Lyon, France;University of Lyon, ERIC, Lyon 2, Lyon, France;LRDSI Laboratory, University of Blida, Saad Dahlab, Blida, Algeria;University of Lyon, ERIC, Lyon 2, Lyon, France
Venue:
Proceedings of the sixteenth international workshop on Data warehousing and OLAP
Year:
2013

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Abstract

Traditional data warehousing technologies and On-Line Analytical Processing (OLAP) are unable to analyze textual data. Moreover, as OLAP queries of a decision-maker are generally related to a context, contextual information must be taken into account during the exploitation of data warehouses. Thus, we propose a contextual text cube model denoted CXT-Cube which considers several contextual factors during the OLAP analysis in order to better consider the contextual information associated with textual data. CXT-Cube is characterized by several contextual dimensions, each one related to a contextual factor. In addition, we extend our aggregation OLAP operator for textual data ORank (OLAP-Rank) to consider all the contextual factors defined in our CXT-Cube model. To validate our model, we perform an experimental study and the preliminary results show the importance of our approach for integrating textual data into a data warehouse and improving the decision-making.