Business Intelligence and Analytics: On-demand ETL over Document Stores
| Autor: | Samira Si-Said Cherfi, Faten Atigui, Manel Souibgui, Sadok Ben Yahia |
|---|---|
| Přispěvatelé: | Centre d'études et de recherche en informatique et communications (CEDRIC), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), Laboratoire d'Informatique, Programmation, Algorithmique et Heuristique (LIPAH), Faculté des Sciences Mathématiques, Physiques et Naturelles de Tunis (FST), Université de Tunis El Manar (UTM)-Université de Tunis El Manar (UTM), CEDRIC. Ingénierie des Systèmes d'Information et de Décision (CEDRIC - ISID), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), Tallinn University of Technology (TTÜ) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Focus (computing)
OLAP Database business.industry Relational database Computer science Online analytical processing Big data InformationSystems_DATABASEMANAGEMENT 02 engineering and technology Document stores computer.software_genre NoSQL ETL Analytics 020204 information systems On demand Business intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing [INFO]Computer Science [cs] Business Intelligence and Analytics business computer |
| Zdroj: | Research Challenges in Information Science 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⟩ Research Challenges in Information Science ISBN: 9783030503154 RCIS |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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