The democratization of data access and the adoption of OLAP in scenarios requiring hand-free interfaces push towards the creation of smart OLAP interfaces. In this paper, we describe COOL, a framework devised for COnversational OLap applications. COOL interprets and translates a natural language dialog into an OLAP session that starts with a GPSJ (Generalized Projection, Selection, and Join) query and continues with the application of OLAP operators. The interpretation relies on a formal grammar and on a repository storing metadata and values from a multidimensional cube. In case of ambiguous text description, COOL can obtain the correct query either through automatic inference or user interactions to disambiguate the text.
Conversational OLAP / Francia M.; Gallinucci E.; Golfarelli M.. - ELETTRONICO. - 2994:(2021), pp. 403-410. (Intervento presentato al convegno 29th Italian Symposium on Advanced Database Systems, SEBD 2021 tenutosi a Pizzo Calabro (VV), Italy nel September 5-9, 2021).
Conversational OLAP
Francia M.
;Gallinucci E.;Golfarelli M.
2021
Abstract
The democratization of data access and the adoption of OLAP in scenarios requiring hand-free interfaces push towards the creation of smart OLAP interfaces. In this paper, we describe COOL, a framework devised for COnversational OLap applications. COOL interprets and translates a natural language dialog into an OLAP session that starts with a GPSJ (Generalized Projection, Selection, and Join) query and continues with the application of OLAP operators. The interpretation relies on a formal grammar and on a repository storing metadata and values from a multidimensional cube. In case of ambiguous text description, COOL can obtain the correct query either through automatic inference or user interactions to disambiguate the text.| File | Dimensione | Formato | |
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