Using ChatGPT to refine draft conceptual schemata in supply-driven design of multidimensional cubes
Stefano Rizzi

TL;DR
This paper investigates whether GPT-4 can assist in the manual refinement of multidimensional data models, showing that prompt engineering improves accuracy but human oversight remains essential.
Contribution
It demonstrates the potential of GPT-4 for automating parts of multidimensional schema refinement through prompt engineering, highlighting current limitations and the need for human involvement.
Findings
Prompt engineering significantly improves GPT-4's refinement accuracy.
Additional prompts can quickly correct residual errors.
Human oversight is still necessary for valid schemata.
Abstract
Refinement is a critical step in supply-driven conceptual design of multidimensional cubes because it can hardly be automated. In fact, it includes steps such as the labeling of attributes as descriptive and the removal of uninteresting attributes, thus relying on the end-users' requirements on the one hand, and on the semantics of measures, dimensions, and attributes on the other. As a consequence, it is normally carried out manually by designers in close collaboration with end-users. The goal of this work is to check whether LLMs can act as facilitators for the refinement task, so as to let it be carried out entirely -- or mostly -- by end-users. The Dimensional Fact Model is the target formalism for our study; as a representative LLM, we use ChatGPT's model GPT-4o. To achieve our goal, we formulate three research questions aimed at (i) understanding the basic competences of ChatGPT…
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Taxonomy
TopicsManufacturing Process and Optimization
