Data Warehouse Design to Support Social Media Analysis in a Big Data Environment

The volume of generated and stored data from social media has increased in the last decade. Therefore, analyzing and understanding this kind of data can offer relevant information in different contexts and can assist researchers and companies in the decision-making process. However, the data are scattered in a large volume, come from different sources, with different formats and are rapidly created. Such facts make the knowledge extraction difficult, turning it in a complex and high costly process. The scientific contribution of this paper is the development of a social media data integration model based on a data warehouse to reduce the computational costs related to data analysis, as well as support the application of techniques to discover useful knowledge. Differently from the literature, we focus on both social media Facebook and Twitter. Also, we contribute with the proposition of a model for the acquisition, transformation and loading data, which can enable the extraction of useful knowledge in a context where the human capability of understanding is exceeded. The results showed that the proposed data warehouse improves the quality of data mining algorithms compared to related works, while being able to reduce the execution time.