Publications of Jérôme Darmont
Reference (inproceedings)
Y. Yang, F. Abdelhédi, J. Darmont, F. Ravat, O. Teste, "Automatic Machine Learning-based OLAP Measure Detection for Tabular Data", 24th International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2022), Vienna, Austria, August 2022; Lecture Notes in Computer Science, Vol. 13428, Springer, Heidelberg, Germany, 173-188.
Abstract
Nowadays, it is difficult for companies and organisations without Business Intelligence (BI) experts to carry out data analyses. Existing automatic data warehouse design methods cannot treat with tabular data commonly defined without schema. Dimensions and hierarchies can still be deduced by detecting functional dependencies, but the detection of measures remains a challenge. To solve this issue, we propose a machine learning-based method to detect measures by defining three categories of features for numerical columns. The method is tested on real-world datasets and with various machine learning algorithms, concluding that random forest performs best for measure detection.
Keywords
Data warehouses, OLAP, Measure detection, Tabular data
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