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Journal Articles Data and Knowledge Engineering Year : 2021

A profile-aware methodological framework for collaborative multidimensional modeling

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Abstract

Multidimensional modeling, i.e., the design of cube schemata, has a key role in data warehouse (DW) projects, in self-service business intelligence, and in general to let users analyze data via the OLAP paradigm. Though an effective involvement of users in multidimensional modeling is crucial in these projects, not much has been said about how to establish a fruitful collaboration in projects involving numerous users with different skills, reputations, and degrees of authority. This issue is especially relevant in citizen science projects, where several volunteers can contribute their requirements despite not being formally-trained experts in the application domain. To fill this gap, we propose a framework for collaborative multidimensional modeling that can adapt itself to the profiles and skills of the actors involved. We first classify users depending on their authoritativeness, skills, and engagement in the project. Then, following this classification, we identify four possible methodological scenarios and propose a profile-aware methodology supported by two sets of quality attributes. Finally, we describe a Group Decision Support System that implements our methodological framework and present some experiments carried out on a real case study.
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Dates and versions

hal-03738398 , version 1 (26-07-2022)

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Cite

Amir Sakka, Sandro Bimonte, Stefano Rizzi, Lucile Sautot, François Pinet, et al.. A profile-aware methodological framework for collaborative multidimensional modeling. Data and Knowledge Engineering, 2021, 131/132, pp.101875. ⟨10.1016/j.datak.2021.101875⟩. ⟨hal-03738398⟩
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