Abstract
Data generated from social network are usually rich and need to be analyzed to support the decision making process. The storage and centralization of these data in a data warehouse are highly required. However, classical warehousing approaches have shown some limitations when dealing with this kind of data. In fact, data warehouses schema are essentially static in that the change of social networks is neglected. Challenged by these limitations, we propose, in this book chapter a methodology to design a DW schema via dynamic discovery of community from huge social networks (Facebook) using semi-supervised hierarchical clustering coupled with profiling ontology.
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Notes
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Towards a new Manner to use Affordable Technologies and Social Networks to Improve Business for Women in Emerging Countries http://projetat.cerist.dz/artisanat/
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Acknowledgments
We are very thankful to the Algerian Tunisian Project dealing with the improvement of handicraft women business in emerging countries through affordable technologies and social networks. This project is financed by the Tunisian Ministry of Higher Education, Scientific Research and Information and Communication Technologies Higher Education and Scientific Research sector.
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Yangui, R., Nabli, A., Gargouri, F. (2017). Community Detection-Based Methodology to Data Warehouse Modeling from Social Network: Application to Handicraft Women Social Network. In: Gaol, F., Hutagalung, F. (eds) Social Interactions and Networking in Cyber Society. Springer, Singapore. https://doi.org/10.1007/978-981-10-4190-7_10
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DOI: https://doi.org/10.1007/978-981-10-4190-7_10
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