The democratization of data access and the adoption of OLAP in scenarios requiring hand-free interfaces push towards the creation of smart OLAP interfaces. To this end, we have create COOL, a system that supports natural language \emph{COnversational OLap} sessions. COOL interprets and translates a natural language dialogue into an OLAP session that starts with a GPSJ (Generalized Projection, Selection and Join) query. The interpretation relies on a formal grammar and a knowledge base storing metadata from a multidimensional cube. COOL is portable, robust, and requires minimal user intervention. It adopts an n-gram based model and a string similarity function to match known entities in the natural language description. In case of incomplete text description, COOL can obtain the correct query either through automatic inference or through interactions with the user to disambiguate the text.
Understand more about COOL trough this demonstration video.
To be published on the day of EDBT’s demo session.