Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Asanka, P.D.: ETL framework design for NoSQL databases in dataware housing. IJRCAR 3, 67–75 (2015)
Baazizi, M.-A., Colazzo, D., Ghelli, G., Sartiani, C.: Parametric schema inference for massive JSON datasets. VLDB J. 28(4), 497–521 (2019). https://doi.org/10.1007/s00778-018-0532-7
Baldacci, L., Golfarelli, M., Graziani, S., Rizzi, S.: QETL: an approach to on-demand ETL from non-owned data sources. Data Knowl. Eng. 112, 17–37 (2017)
Chevalier, M., Malki, M.E., Kopliku, A., Teste, O., Tournier, R.: Document-oriented models for data warehouses - NoSQL document-oriented for data warehouses. In: ICEIS 2016 - Proceedings of the 18th International Conference on Enterprise Information Systems, Rome, Italy, 25–28 April, vol. 1, pp. 142–149 (2016)
Chouder, M.L., Rizzi, S., Chalal, R.: Exodus: exploratory OLAP over document stores. Inf. Syst. 79, 44–57 (2019)
Gallinucci, E., Golfarelli, M., Rizzi, S.: Schema profiling of document-oriented databases. Inf. Syst. 75, 13–25 (2018)
Gallinucci, E., Golfarelli, M., Rizzi, S.: Approximate OLAP of document-oriented databases: a variety-aware approach. Inf. Syst. 85, 114–130 (2019)
Mallek, H., Ghozzi, F., Teste, O., Gargouri, F.: BigDimETL with NoSQL database. In: Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 22nd International Conference KES, Belgrade, Serbia, pp. 798–807 (2018)
Souibgui, M., Atigui, F., Zammali, S., Cherfi, S.S., Ben Yahia, S.: Data quality in ETL process: a preliminary study. In: Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 23rd International Conference KES-2019, Budapest, Hungary, pp. 676–687 (2019)
Yang, Y., Meneghetti, N., Fehling, R., Liu, Z.H., Kennedy, O.: Lenses: an on-demand approach to ETL. PVLDB 8(12), 1578–1589 (2015)
Yangui, R., Nabli, A., Gargouri, F.: ETL based framework for NoSQL warehousing. In: Themistocleous, M., Morabito, V. (eds.) EMCIS 2017. LNBIP, vol. 299, pp. 40–53. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65930-5_4
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Souibgui, M., Atigui, F., Yahia, S.B., Si-Said Cherfi, S. (2020). Business Intelligence and Analytics: On-demand ETL over Document Stores. In: Dalpiaz, F., Zdravkovic, J., Loucopoulos, P. (eds) Research Challenges in Information Science. RCIS 2020. Lecture Notes in Business Information Processing, vol 385. Springer, Cham. https://doi.org/10.1007/978-3-030-50316-1_38
Download citation
DOI: https://doi.org/10.1007/978-3-030-50316-1_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-50315-4
Online ISBN: 978-3-030-50316-1
eBook Packages: Computer ScienceComputer Science (R0)