Carrying out OLAP analyses in hands-free scenarios requires lean forms of communication between the users and the system, based for instance on natural language. In this paper we introduce VOOL, a framework specifically devised for vocalizing the insights resulting from OLAP sessions. VOOL is self-configurable, extensible, and is aware of the user’s intentions expressed by OLAP operators. To avoid overwhelming the user with very long descriptions, we pursue the vocalization of selected insights automatically extracted from query results. These insights are detected by a set of modules, each returning a set of independent insights that characterize data. After describing and formalizing our approach, we evaluate it in terms of efficiency and effectiveness.

Insight-based vocalization of OLAP sessions

Matteo Francia;Enrico Gallinucci;Matteo Golfarelli;Stefano Rizzi
2022

Abstract

Carrying out OLAP analyses in hands-free scenarios requires lean forms of communication between the users and the system, based for instance on natural language. In this paper we introduce VOOL, a framework specifically devised for vocalizing the insights resulting from OLAP sessions. VOOL is self-configurable, extensible, and is aware of the user’s intentions expressed by OLAP operators. To avoid overwhelming the user with very long descriptions, we pursue the vocalization of selected insights automatically extracted from query results. These insights are detected by a set of modules, each returning a set of independent insights that characterize data. After describing and formalizing our approach, we evaluate it in terms of efficiency and effectiveness.
Proceedings 26th East-European Conference on Advances in Databases and Information Systems
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Matteo Francia, Enrico Gallinucci, Matteo Golfarelli, Stefano Rizzi
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/892066
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