Automated Keyword Generation Using LLMs in a High-Volume Data Virtualization Service
TL;DR
This work proposes five strategies for selecting different collections of text data for processing by the large language models (LLMs) and applies them to virtual datasets created in five application case studies with different source datasets.
Abstract
In modern data infrastructure, a data virtualization service (DVS) allows users to create many versions of source datasets for different processing tasks. For example, a multivariate or multi-modality dataset can be used for different machine learning tasks (e.g., classification, detection, segmentation, forecasting, and so on). Data selection, participation, integration, and preprocessing in different tasks result in many virtual datasets [Khan et al., SSC 2026]. Meanwhile, different users need to discover existing virtual datasets frequently and easily to avoid duplicate efforts. To enable an effective search engine in a DVS, it is desirable to automatically generate keywords for each virtual dataset. In this work, we present a solution for generating keywords for virtual datasets using large language models (LLMs). We propose five strategies for selecting different collections of text data for processing by the LLMs. We applied the five strategies to virtual datasets created in five application case studies with different source datasets. We conducted experiments with three LLM generators and two embedding techniques. The experiments show that the performances of the five strategies depend on the quality of the text descriptions. The results help us adopt a multi-strategy implementation of a new keyword generation service (KGS) as part of an extended DVS. The work demonstrates that the capability of automated keyword generation is indispensable for a high-volume DVS that is dataset-rich and frequently accessed.
