Abstract
Web 2.0 technologies have brought new ways of connecting people in social networks for collaboration and communication in various on-line communities. Social network analysis (SNA) is used to model social relationships as nodes (individuals, organizations: actors) and edges (relationships between these nodes). This analysis is based on a structural approach in order to describe relations between Facebook members (communication, collaboration, cohesion, centrality of members, etc.) using a set of SNA metrics. Decisional systems such as Data WeBHouse (DWB), known by their data-consuming, must be enriched by this kind of metrics to give better help to decision makers. So, several steps of the DWB life cycle must be revised to integrate this social data. In this paper, we propose a modeling approach for Social Data WeBHouse (SDWB) based on social data. Our multidimensional schema integrates SNA metrics applied on graphs of Facebook pages and groups. We have implemented two techniques to analyze our SDWB which are the Online Analytical Process (OLAP) analysis and the contextualized association rules. The result of these analyses helps decision makers to identify the most collaborative members in groups and pages. Our approach is validated by an SDWB prototype.
Published Version
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