GO-GenZip: Goal-Oriented Generative Sampling and Hybrid Compression

• URL: http://arxiv.org/abs/2603.20109v1
• Date: Fri, 20 Mar 2026 16:33:15 GMT
• Title: GO-GenZip: Goal-Oriented Generative Sampling and Hybrid Compression
• Abstract summary: Current network data telemetry pipelines consist of massive streams of fine-grained Key Performance Indicators (KPIs) from multiple distributed sources towards central aggregators.<n>This work presents a generative AI (GenAI)-driven sampling and hybrid compression framework that redesigns network telemetry from a goal-oriented perspective.
• Score: 50.76941829813458
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current network data telemetry pipelines consist of massive streams of fine-grained Key Performance Indicators (KPIs) from multiple distributed sources towards central aggregators, making data storage, transmission, and real-time analysis increasingly unsustainable. This work presents a generative AI (GenAI)-driven sampling and hybrid compression framework that redesigns network telemetry from a goal-oriented perspective. Unlike conventional approaches that passively compress fully observed data, our approach jointly optimizes what to observe and how to encode it, guided by the relevance of information to downstream tasks. The framework integrates adaptive sampling policies, using adaptive masking techniques, with generative modeling to identify patterns and preserve critical features across temporal and spatial dimensions. The selectively acquired data are further processed through a hybrid compression scheme that combines traditional lossless coding with GenAI-driven, lossy compression. Experimental results on real network datasets demonstrate over 50$\%$ reductions in sampling and data transfer costs, while maintaining comparable reconstruction accuracy and goal-oriented analytical fidelity in downstream tasks.

Related papers

• TDGT: A Tabular Data Generation Toolkit supporting adaptive GPU-accelerated Bayesian mixture models, diffusion-based models, and latent-space generative modeling [1.5441436657738097]
  TDGT is a web-based toolkit for synthetic data generation and fidelity assessment.<n>It introduces the Adaptive Bayesian Mixture Synthesizer (ABMS), a novel algorithm that autonomously determines the optimal number of mixture components.<n> TDGT is assessed across datasets from healthcare, socioeconomic modeling, and cybersecurity domains.
  arXiv  Detail & Related papers  (2026-06-30T07:42:48Z)
• Synthesizing real-world distributions from high-dimensional Gaussian Noise with Fully Connected Neural Network [0.2538209532048867]
  This work proposes a time-efficient synthetic data generation method based on a fully connected neural network and a randomized loss function.<n>The experiments conducted on 25 diverse real-world datasets confirm that the proposed solution surpasses the state-of-the-art generative methods.
  arXiv  Detail & Related papers  (2026-04-10T08:20:45Z)
• UtilGen: Utility-Centric Generative Data Augmentation with Dual-Level Task Adaptation [70.2215233759276]
  UtilGen is a novel utility-centric data augmentation framework for computer vision tasks.<n>We show that UtilGen consistently achieves superior datasets, with an average accuracy improvement of 3.87% over previous SOTA.<n>Further analysis of data influence and distribution reveals that UtilGen produces more impactful and task-relevant synthetic data.
  arXiv  Detail & Related papers  (2025-10-28T10:17:11Z)
• Knowledge-Informed Neural Network for Complex-Valued SAR Image Recognition [51.03674130115878]
  We introduce the Knowledge-Informed Neural Network (KINN), a lightweight framework built upon a novel "compression-aggregation-compression" architecture.<n>KINN establishes a state-of-the-art in parameter-efficient recognition, offering exceptional generalization in data-scarce and out-of-distribution scenarios.
  arXiv  Detail & Related papers  (2025-10-23T07:12:26Z)
• Single Domain Generalization with Model-aware Parametric Batch-wise Mixup [22.709796153794507]
  Single Domain Generalization remains a formidable challenge in the field of machine learning.<n>We propose a novel data augmentation approach, named as Model-aware Parametric Batch-wise Mixup.<n>By exploiting inter-feature correlations, the parameterized mixup generator introduces additional versatility in combining features across a batch of instances.
  arXiv  Detail & Related papers  (2025-02-22T03:45:18Z)
• Neural Conformal Control for Time Series Forecasting [54.96087475179419]
  We introduce a neural network conformal prediction method for time series that enhances adaptivity in non-stationary environments.<n>Our approach acts as a neural controller designed to achieve desired target coverage, leveraging auxiliary multi-view data with neural network encoders.<n>We empirically demonstrate significant improvements in coverage and probabilistic accuracy, and find that our method is the only one that combines good calibration with consistency in prediction intervals.
  arXiv  Detail & Related papers  (2024-12-24T03:56:25Z)
• Enabling robust sensor network design with data processing and optimization making use of local beehive image and video files [0.0]
  We of er a revolutionary paradigm that uses cutting-edge edge computing techniques to optimize data transmission and storage. Our approach encompasses data compression for images and videos, coupled with a data aggregation technique for numerical data. A key aspect of our approach is its ability to operate in resource-constrained environments.
  arXiv  Detail & Related papers  (2024-02-26T15:27:47Z)
• CONVERT:Contrastive Graph Clustering with Reliable Augmentation [110.46658439733106]
  We propose a novel CONtrastiVe Graph ClustEring network with Reliable AugmenTation (CONVERT) In our method, the data augmentations are processed by the proposed reversible perturb-recover network. To further guarantee the reliability of semantics, a novel semantic loss is presented to constrain the network.
  arXiv  Detail & Related papers  (2023-08-17T13:07:09Z)
• Information-Ordered Bottlenecks for Adaptive Semantic Compression [0.0]
  We present a neural layer designed to adaptively compress data into variables ordered by likelihood. We show that IOBs achieve near-optimal compression for a given architecture and can assign encoding signals in a manner that is semantically meaningful. We introduce a novel theory for estimating global dimensionality with IOBs and show that they recover SOTA dimensionality estimates for complex synthetic data.
  arXiv  Detail & Related papers  (2023-05-18T18:00:00Z)
• Neural BRDF Representation and Importance Sampling [79.84316447473873]
  We present a compact neural network-based representation of reflectance BRDF data. We encode BRDFs as lightweight networks, and propose a training scheme with adaptive angular sampling. We evaluate encoding results on isotropic and anisotropic BRDFs from multiple real-world datasets.
  arXiv  Detail & Related papers  (2021-02-11T12:00:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.