A profile-aware methodological framework for collaborative multidimensional modeling

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