Abstract
The integration of multidimensional data and machine learning seems to be natural in the area of business intelligence. On-Line Analytical Processing (OLAP) tools are frequent in this area where the data are usually represented in multidimensional datamarts and data mining tools are integrated in some of these tools. However, the efforts for a full integration of data mining and OLAP tools have not been as common as originally expected. Nowadays, this integration is mostly carried out on source code, implementing solutions that perform (i) all the operations on multidimensional data as well as (ii) the data mining algorithms to extract knowledge from these data. Hence, there now exists an important distinction between implementation-based developments where the entire solution is implemented on source code and OLAP-tool-based developments where (at least) the operations on multidimensional data are performed using an OLAP tool. This work analyses these two alternatives in cost-effective terms, performing an experimental analysis on a multidimensional problem and discussing when each approach seems to excel the other.
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Notes
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We use data cube independently from the number of dimensions, although we often find this term as hypercube when more than 3 dimensions are involved in the hierarchy.
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See https://github.com/overcoil/X4R/issues/ for a complete list of issues with this package.
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Concretely, we used biserver-ce-5.2.0.0-209 version of this software, which is not the last version but compatible. Visit http://community.pentaho.com/ for details.
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This is only necessary if, as often happens, you have attribute names written down using underscores.
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Acknowledgements
We thank the anonymous reviewers for their comments, which have helped to improve this paper. This work was supported by the Spanish MINECO under grant TIN 2013-45732-C4-1-P and by Generalitat Valenciana PROMETEOII2015/ 013. This research has been developed within the REFRAME project, granted by the European Coordinated Research on Long-term Challenges in Information and Communication Sciences & Technologies ERA-Net (CHIST-ERA), and funded by the Ministerio de Economía y Competitividad in Spain (PCIN-2013-037).
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Martínez-Usó, A., Hernández-Orallo, J., Ramírez-Quintana, M.J., Plumed, F.M. (2015). Pentaho + R: An Integral View for Multidimensional Prediction Models. In: Puerta, J., et al. Advances in Artificial Intelligence. CAEPIA 2015. Lecture Notes in Computer Science(), vol 9422. Springer, Cham. https://doi.org/10.1007/978-3-319-24598-0_21
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