Abstract
In this paper, we aim to optimally identify the analyst’groups in data warehouse. For that reason, we study the similarity between the selected queries in the analytical history. Four axis for group identification are distinguished: (i) the function exerted, (ii) the granted responsibilities to accomplish goals, (iii) the source of groups identification, (iv) the dynamicity of discovered groups. A semi-supervised hierarchical algorithm is used to discover the most discriminating criterion. Carried out experiments on real data warehouse demonstrate that groupization improves upon personalization for several group types, mainly for function-based groupization.
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Ben Ahmed, E., Nabli, A., Gargouri, F. (2012). Performing Groupization in Data Warehouses: Which Discriminating Criterion to Select?. In: Bouma, G., Ittoo, A., Métais, E., Wortmann, H. (eds) Natural Language Processing and Information Systems. NLDB 2012. Lecture Notes in Computer Science, vol 7337. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31178-9_27
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DOI: https://doi.org/10.1007/978-3-642-31178-9_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31177-2
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