mlsanityData to 7 Oct 2026

GAN-based Tabular Data Generator for Constructing Synopsis in Approximate Query Processing: Challenges and Solutions

arXiv 2212.09015 ↗First posted 18 Dec 2022cs.DB cs.LGcited by 4

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Abstract (arXiv)

In data-driven systems, data exploration is imperative for making real-time decisions. However, big data is stored in massive databases that are difficult to retrieve. Approximate Query Processing (AQP) is a technique for providing approximate answers to aggregate queries based on a summary of the data (synopsis) that closely replicates the behavior of the actual data, which can be useful where an approximate answer to the queries would be acceptable in a fraction of the real execution time. This study explores the novel utilization of Generative Adversarial Networks (GANs) in the generation of tabular data that can be employed in AQP for synopsis construction. We thoroughly investigate the unique challenges posed by the synopsis construction process, including maintaining data distribution characteristics, handling bounded continuous and categorical data, and preserving semantic relationships and then introduce the advancement of tabular GAN architectures that overcome these challenges. Furthermore, we propose and validate a suite of statistical metrics tailored for assessing the reliability of the GAN-generated synopses. Our findings demonstrate that advanced GAN variations exhibit a promising capacity to generate high-fidelity synopses, potentially transforming the efficiency and effectiveness of AQP in data-driven systems.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 23 of its 52 references that are in the map. First posted on arXiv 18 Dec 2022.

Builds on

23 of 52 references.

References · 23

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11 more
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Cited by year

Citing papers per year: 2023: 2; 2024: 2.

4 papers.

  1. 2023Explain To Decide: A Human-Centric Review on the Role of Explainable Artificial Intelligence in AI-assisted Decision Makingcited by 2
  2. 2024zGAN: An Outlier-focused Generative Adversarial Network For Realistic Synthetic Data Generationcited by 2
  3. 2023Collaboration in Immersive Environments: Challenges and Solutionscited by 1
  4. 2024User Intent Recognition and Semantic Cache Optimization-Based Query Processing Framework using CFLIS and MGR-LAUcited by 0