Towards AI-Powered Multi-Modal Generative OLAP

Recent advances in AI allow decision makers to move beyond traditional analysis towards sophisticated decision- making tasks that require human intuition and perception. This opens the door to a novel form of OLAP, one that integrates unstructured data —such as text and images— into its analytical workflows. In this work we propose OLAP-AI, a novel and enriched form of the OLAP paradigm aimed at supporting, besides the analysis of categorical and numeric data, also semantically rich operations over free text and images. OLAP-AI is multi-modal, in that it supports crossed semantic searches between text and images for filtering and grouping. Besides, it significantly extends aggregation by operating on text and images rather than on numeric data only, and by relying on generative models. To investigate the technical feasibility of the OLAP-AI paradigm, we provide a proof-of-concept by relying on the open-source vector DBMS Weaviate.