Abstract
The modeling process for text-data type used for analysis purposes is to give a special representation for this kind of unstructured data. The given representation offers a formal description for text data to enable an effective use of the information contained in the text. In this context, and in order to perform analysis on this unstructured data type, we propose the multidimensional semantic model (MSMTO). The proposed model is based on the object paradigm. The model integrates a new concept Semantic Content Object used to represent and organize the semantic of text data in a hierarchical format, to enable a semantic analysis at different levels of granularity. Our modeling approach considers the internal composition of text documents as a structural hierarchy, which allows the user to perform analysis on different hierarchical levels. Our model offers also flexibility, by considering the semantic content of text-data as a measure, a fact or even a dimension.
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References
Attaf, S., Benblidia, N.: Modelisation multidimensionnelle des donnees textuelles ou en sommesnous? In: ASD Conference Proceedings, Conference maghrebine sur les avancees des systemes decisionnels, pp. 3–25 (2013)
MartÃn-Bautista, M.J., Molina, C., Tejeda, E., Vila, M.A.: Using textual dimensions in data warehousing processes. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds.) IPMU 2010. CCIS, vol. 81, pp. 158–167. Springer, Heidelberg (2010)
Blei, D.M., Ng, A., Jordan, M.: Latent dirichlet allocation. Journal of Machine Learning Research 3(2), 993–1022 (2003)
Boukraa, D., Boussaid, O., Bentayeb, F., Zegour, D.: Modle multidimensionnel d’objets complexes: Du modele d’objets aux cubes d’objets complexes. Ingénierie des Systèmes d’Information 16 (2011)
Kimball, R.: The data warehouse toolkit: Practical Techniques for Building Dimensional Data Warehouses. John Wiley and Sons (1996)
Lin, C.X., Ding, B., Han, J., Zhu, F., Zhao, B.: Text cube: Computing ir measures for multidimensional text database analysis. In: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, pp. 905–910 (2008)
Mothe, J., Chrisment, C., Dousset, B., Alaux, J.: Doccube: Multi-dimensional visualisation and exploration of large document sets. Journal of the American Society for Information Science and Technology 54, 650–659 (2003)
Park, B.-K., Han, H., Song, I.-Y.: Xml-olap: A multidimensional analysis framework for xml warehouses. In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2005. LNCS, vol. 3589, pp. 32–42. Springer, Heidelberg (2005)
Tounier, R.: Analyse en ligne (OLAP) de documents. Thèse de doctorat, Université Toulouse III. Paul Sabatier (2007)
Zhang, D., Zhai, C., Han, J.: Topic cube: Topic modeling for olap on multidimensional text databases. In: SDM 2009: Proceedings of the 2009 SIAM International Conference on Data Mining, Sparks, NV, USA, pp. 1124–1135 (2009)
Zhang, D., Zhai, C., Han, J.: Mitexcube:microtextcluster cube for online analysis of text cells. In: The NASA Conference on Intelligent Data Understanding (CIDU), pp. 204–218 (2011)
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Attaf, S., Benblidia, N., Boussaid, O. (2014). The Multidimensional Semantic Model of Text Objects(MSMTO): A Framework for Text Data Analysis. In: Ait Ameur, Y., Bellatreche, L., Papadopoulos, G.A. (eds) Model and Data Engineering. MEDI 2014. Lecture Notes in Computer Science, vol 8748. Springer, Cham. https://doi.org/10.1007/978-3-319-11587-0_12
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DOI: https://doi.org/10.1007/978-3-319-11587-0_12
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