Performing groupization in data warehouses: which discriminating criterion to select?

Authors:
Eya Ben Ahmed;Ahlem Nabli;Faïez Gargouri
Affiliations:
Sfax University, Tunisia;Sfax University, Tunisia;Sfax University, Tunisia
Venue:
NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems
Year:
2012

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A new semi-supervised hierarchical active clustering based on ranking constraints for analysts groupization

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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.