<p>Large-scale, multi-dimensional mixed datasets are characterized by the pervasive "long-tail distribution." This phenomenon results in data sparsity in subspaces defined by multi-dimensional attribute combinations. This sparsity severely hinders data-driven analysis and insights. Existing data-augmentation methods primarily focus on single dimensions, ignoring the complex, intrinsic multi-dimensional correlations of the real world, which leads to generated samples that lack logic and realism. To address this challenge, we propose a systematic, on-demand, and fine-grained sample augmentation framework. Our core idea is to precisely locate and augment data-sparse regions from a multi-dimensional combinatorial perspective. For textual data, we have designe two flexible augmentation modes. The first, "augmentation by subset scope", adopts a strategy of model merging and incremental updates. The second, "augmentation by topic", proposes a heuristic search algorithm based on the "Explore-Exploit" paradigm. For numerical data, we pre-construct a global distribution index to achieve efficient identification of sparse intervals. In the sample generation phase, we combine Large Language Models, Retrieval-Augmented Generation, and Chain-of-Thought techniques to ensure that the generated samples meet high-fidelity standards in semantic logic and contextual style. Extensive experiments on real-world datasets demonstrate that our method outperforms baseline approaches in query response speed and the efficiency of sparse region discovery, while maintaining topic coherence and accuracy.</p>