Jun 2026· Educational Data Mining· pp. 1-6· 0 citations· 12 references
Abstract
The generation of synthetic tabular data has emerged as a critical technique for addressing data scarcity, privacy concerns, and the need for augmented training datasets in machine learning applications. This study investigates the applicability and effectiveness of state-of-the-art generative models for synthesizing performance metrics of data storage systems. We utilize a performance dataset encompassing hard disk drive sequential storage configurations characterized by critical indicators including input/output operations per second and latency measurements. Our research methodology follows a systematic approach. First, we establish a baseline using the Synthetic Data Vault library to understand fundamental generative capabilities for tabular data. Subsequently, we implement and evaluate three advanced diffusion-based architectures and one generative adversarial network approach: TabSyn, TabDiff, TabDDPM, and CTGAN. The experimental framework encompasses comprehensive quality assessment through both visual inspection and quantitative metrics. Visual evaluation includes comparative analysis of input/output operations per second and latency distributions, marginal distributions of feature values, and correlation structure preservation between synthetic and real datasets. Quantitative assessment leverages detection scores, shape and trend similarity measures, and machine learning efficacy scores. The experimental results provide insights into the strengths and limitations of each generative approach when applied to storage system performance data exhibiting complex multi-modal distributions and intricate feature correlations.
A novel quantum generative model for synthesizing tabular data by proposing a quantum generative adversarial network architecture with flexible data encoding and a novel quantum circuit ansatz for effectively modeling tabular data is introduced.
P. Bhardwaj, Caitlin Jones, Lasse Dierich et al.· Scientific Reports· 2 citations
Nine sampling strategies are benchmarked on three real-world datasets and representativeness, not domain knowledge, determines sampler quality -- schema-free random uniform or cluster sampling suffices for production-grade quality profiling at scale.
This first comprehensive study of data heterogeneity and lossless general-purpose compression performance for representative datasets from the PETRA III synchrotron radiation source provides a quantitative basis for future archival and storage decisions at PETRA III, its future successor, PETRA IV, and other large-scale scientific facilities.
M. Buschmann, Yannis Schumann, Christian Voss et al.· 0 citations
Synthetic tabular data support use cases like data sharing, model development under access restrictions, and rapid prototyping of analytical workflows. Modern generative models are evaluated by their statistical similarity, correlation structure, privacy, and downstream machine-learning utility. However, such evaluations leave a gap: they rarely test the structure that matters for analytical queries. We present TabQueryBench, a query-centric benchmark that uses SQL-shaped analytical queries as structural assessors for synthetic data fidelity. It provides an extensible foundation for query-centric synthetic-data evaluation. From 12 public sources of analytical queries, TabQueryBench taxonomizes recurring cross-domain logic into 44 reusable query templates and grounds them to each dataset via a policy-guided template-to-SQL pipeline. This makes queries schema-aware while preserving comparability across generative models. Across 49 datasets and 11 generative models, it activates 10-12 templates per dataset, producing more than 100 executable SQL queries per dataset. Our systematic experiments show five main patterns. First, current tabular generative models can have good distance-based fidelity, but they still fall short on query-centric fidelity: RealTabFormer achieves the highest query-centric fidelity, but it only reaches 0.75 +/- 0.15 (REAL data score is 1.00). Second, tabular generative models struggle with very high-cardinality discrete support. Third, SOTA generative models preserve good global conditional query-centric fidelity, but fail more on local queries. Fourth, tail fidelity deteriorates as queries move toward the extreme tail; even the best model recovers only about 40.7% of real rare values. Finally, there is a fidelity-cost tradeoff in tabular generation: BayesNet offers the strongest tradeoff, with slightly lower query-centric fidelity but much lower generation cost.
Jialin Zhang, Fenghao Dong, Yajie Zhou et al.· 0 citations
Synthetic tabular data is increasingly used to support data sharing and machine learning development, where access to real data is restricted or sensitive. However, existing tabular data generators often require dataset-specific training or fine-tuning, data preprocessing, and extensive hyperparameter tuning to achieve plausible results. In addition, existing models may still struggle with mixed data types, modeling complex feature relationships, and pose a privacy risk for information leakage. With the rise of Large Language Models (LLMs) and their expanding capabilities across many domains, we investigate whether few-shot LLMs can be used in a training-free setting as tabular data generators. We propose FewShotTabLLM, a few-shot, training-free generation framework for tabular data that leverages schema-enriched context, representative sampling, and dataset profiling. We evaluate our approach across five widely used tabular benchmark datasets and compare it against strong training-based baselines from different model families (CTGAN, TVAE, TabDDPM, BeGReaT). A three-axis evaluation protocol has been used that covers representativeness, realism, and privacy. Across all evaluations, FewShotTabLLM outperforms baselines and surpasses them on many datasets, achieving realism, boundary adherence, and privacy preservation across all experiments. Overall, the results suggest that training-free fewshot LLM synthesis can serve as an alternative to existing models and a prominent research direction for improving dependency preservation and privacy-aware generation. Our code is available at: https://github.com/BMW-InnovationLab/FewShotTabLLM
Hadi Koubeissy, Michel El Khoury, Marc Kamradt et al.· Annual International Compute...· 0 citations
Machine learning pipelines are increasingly deployed in high-stakes domains where robustness against data inconsistencies, bias, and distributional shifts is crucial. However, real-world datasets often suffer from limitations such as data scarcity, class imbalance, and privacy constraints. Synthetic data generation has emerged as a promising strategy to overcome these challenges and enhance pipeline robustness. This paper presents a comprehensive survey and analysis of synthetic data generation techniques, classifying them by data modality, generation method, and application purpose. We explore how synthetic data contributes to the resilience of ML pipelines against failure modes such as concept drift, noise, and adversarial attacks. Through empirical case studies across domains, we demonstrate the practical benefits and limitations of integrating synthetic data into training and evaluation pipelines. Finally, we discuss ethical considerations and outline future research directions toward building more robust, fair, and privacy-preserving ML systems using synthetic data.
Dennis M. Ritchie, Allen Newell· International Journal of Dat...· 0 citations