The Intentional Analytics Model (IAM) has been devised to couple OLAP and analytics by (i) letting users express their analysis intentions on multidimensional data cubes and (ii) returning enhanced cubes, i.e., multidimensional data annotated with knowledge insights in the form of models (e.g., correlations). Five intention operators were proposed to this end; of these, describe and assess have been investigated in previous papers. In this work we enrich the IAM picture by focusing on the explain operator, whose goal is to provide an answer to the user asking "why does measure m show these values?". Specifically, we propose a syntax for the operator and discuss how enhanced cubes are built by (i) finding the polynomials that best approximate the relationship between m and the other cube measures, and (ii) highlighting the most interesting one. Finally, we test the operator implementation in terms of efficiency.

The Whys and Wherefores of Cubes / Matteo Francia, Stefano Rizzi, Patrick Marcel. - ELETTRONICO. - 3369:(2023), pp. 43-50. (Intervento presentato al convegno 25th International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data (DOLAP 2023) tenutosi a Ioannina, Greece nel March 28, 2023).

The Whys and Wherefores of Cubes

Matteo Francia;Stefano Rizzi
;
2023

Abstract

The Intentional Analytics Model (IAM) has been devised to couple OLAP and analytics by (i) letting users express their analysis intentions on multidimensional data cubes and (ii) returning enhanced cubes, i.e., multidimensional data annotated with knowledge insights in the form of models (e.g., correlations). Five intention operators were proposed to this end; of these, describe and assess have been investigated in previous papers. In this work we enrich the IAM picture by focusing on the explain operator, whose goal is to provide an answer to the user asking "why does measure m show these values?". Specifically, we propose a syntax for the operator and discuss how enhanced cubes are built by (i) finding the polynomials that best approximate the relationship between m and the other cube measures, and (ii) highlighting the most interesting one. Finally, we test the operator implementation in terms of efficiency.
2023
Proceedings of the 25th International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data (DOLAP)
43
50
The Whys and Wherefores of Cubes / Matteo Francia, Stefano Rizzi, Patrick Marcel. - ELETTRONICO. - 3369:(2023), pp. 43-50. (Intervento presentato al convegno 25th International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data (DOLAP 2023) tenutosi a Ioannina, Greece nel March 28, 2023).
Matteo Francia, Stefano Rizzi, Patrick Marcel
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/920791
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