Business Intelligence and Analytics: On-demand ETL over Document Stores

Manel Souibgui (1, 2) , Faten Atigui (3) , Sadok Ben Yahia (2, 4) , Samira Si-Said Cherfi (3)

1 CEDRIC - Centre d'études et de recherche en informatique et communications
2 LIPAH - Laboratoire d'Informatique, Programmation, Algorithmique et Heuristique
3 CEDRIC - ISID - CEDRIC. Ingénierie des Systèmes d'Information et de Décision
4 TTÜ - Tallinn University of Technology

For many decades, Business Intelligence and Analytics (BI&A) has been associated with relational databases. In the era of big data and NoSQL stores, it is important to provide approaches and systems capable of analyzing this type of data for decision-making. In this paper, we present a new BI&A approach that both: (i) extracts, transforms and loads the required data for OLAP analysis (on-demand ETL) from document stores, and (ii) provides the models and the systems required for suitable OLAP analysis. We focus here, on the on-demand ETL stage where, unlike existing works, we consider the dispersion of data over two or more collections.

Keywords: Business Intelligence and Analytics; Document stores; ETL; OLAP

Domains: Computer Science [cs]

Manel Souibgui, Faten Atigui, Sadok Ben Yahia, Samira Si-Said Cherfi. Business Intelligence and Analytics: On-demand ETL over Document Stores. Research Challenges in Information Science, Sep 2020, Limassol, Cyprus. Springer, 385, pp.556-561, 2020, Lecture Notes in Business Information Processing. ⟨10.1007/978-3-030-50316-1_38⟩. ⟨hal-03026348⟩