Towards Data Warehouse Schema Design from Social Networks - Dynamic Discovery of Multidimensional Concepts

Abstract

This research work is conducting as part of the project BWEC (Business for Women in Women of Emerging Country) that aims to improve the socioeconomic situation of handicraft women by providing true technological means. In fact, since few years, the Web has been transformed into an exchange platform where users have become the main suppliers of information through social media. User-generated data are usually rich and thus need to be analyzed to enhance decision. The storage and the centralization of these data in a data warehouse (DW) are highly required. Nevertheless, the growing complexity and volumes of the data to be analyzed impose new requirements on DW. In order to address these issues, in this paper, we propose four stages methodology to define a DW schema from social networks. Firstly we design the initial DW schema based on the existing approaches. Secondly, we apply a set of transformation rules to prepare the creation of the NOSQL(Not Only SQL) data warehouse. Then, based on user's requirement, clustering of social networks profiling data will be performed which allows the dynamic discovery of multidimensional concepts. Finally, the enrichment of the NoSQL DW schema by the discovered MC will be realized to ensure the DW schema evolution.

Optimization of Columnar NoSQL Data Warehouse Model with Clarans Clustering Algorithm

“…In order to perfectly meet the needs of business leaders in the big data era, several researches have been made to ensure the enrichment of the decisional systems with different types of big data such as semantic web [4], social data [5,6], NoSQL data [7], data lakes [8] and LOD [9,10,11]. However, neither of these approaches has implemented a decision-making system based on a big data warehouse, which is ready at all times to support the increased integration of big data so as to constantly satisfy the functional exigencies of decision-makers.…”

Large-Scale System for Social Media Data Warehousing

“…Its measures are among others, number of shares, number of likes, number of followers. In (Yangui et al, 2015;Yangui et al, 2017), and based on that a multidimensional model for a social data warehouse should be generated dynamically from the changing and evolving social networks structure, authors proposed DW4SN system. The system is composed of five modules which starts from a classical data warehouse schema and discover multidimensional concepts using a semi supervised clustering approach.…”