Integrating AI and the Multidimensional Model: The Cube+AI Framework

The integration of AI models into DBMSs has recently been proposed and implemented in tools such as PostgresAI. However, these solutions do not take full advantage of the multidimensional vision of information typically adopted in complex analytical tasks. At the same time, AI offers a chance to reconsider and improve analytical tasks based on the multidimensional model. In this work, we propose Cube+AI, a fresh vision of the multidimensional model aimed at extending the expressive power of cube queries to include semantically rich and multimodal operations on free text and images in addition to the analysis of categorical and numerical data. Cube+AI supports crossed semantic searches between text and images for filtering and grouping; moreover, it significantly extends aggregation by operating on text and images rather than on numeric data only. Thus, while the multidimensional model provides the structured context necessary to enhance AI execution, AI models expand the multidimensional paradigm by enabling the analysis and aggregation of unstructured data. The Cube+AI framework includes (i) a formal extension of the multidimensional model, which supports the dynamical definition of virtual categorical levels derived at query time from other levels via AI, as well as (ii) a text-to-SQL method where an LLM is leveraged to translate natural language queries into SQL. Specifically, we propose an implementation that relies on PostgresAI as a DBMS and on the Gemini LLM as a natural language querying interface. The paper is completed by the discussion of a set of experiments made with a sample workload of queries and by some robustness tests.