构建合成数据集，将网络指标转为图像，助力未来网络可视化分析
SliceVision-F2I: A Synthetic Feature-to-Image Dataset for Visual Pattern Representation on Network Slices
- 用四种方法将多维网络指标转为低分辨率图像
- 每种方法生成3万张带原始指标的图像样本
- 适合做网络异常检测和图像化学习研究
5G与6G网络推动网络切片成为未来服务架构的核心，亟需可靠的识别方法与数据支持。本文提出SliceVision-F2I，一个用于下一代网络系统中网络切片特征可视化的合成样本数据集。该数据集通过四种编码方式（物理启发映射、Perlin噪声、神经壁纸化、分形分支）将多变量关键性能指标（KPI）向量转换为视觉表示。每种方法生成30,000个样本，每个样本包含原始KPI向量与对应低分辨率RGB图像。数据集模拟真实且带有噪声的网络环境，反映实际运行中的不确定性和测量误差。该数据集适用于视觉学习、网络状态分类、异常检测及基于图像的机器学习方法基准测试。数据集公开可获取，可用于多变量时间序列分析、合成数据生成与特征到图像转换等研究场景。
原文摘要 · Abstract (English)
The emergence of 5G and 6G networks has established network slicing as a significant part of future service-oriented architectures, demanding refined identification methods supported by robust datasets. The article presents SliceVision-F2I, a dataset of synthetic samples for studying feature visualization in network slicing for next-generation networking systems. The dataset transforms multivariate Key Performance Indicator (KPI) vectors into visual representations through four distinct encoding methods: physically inspired mappings, Perlin noise, neural wallpapering, and fractal branching. For each encoding method, 30,000 samples are generated, each comprising a raw KPI vector and a corresponding RGB image at low-resolution pixels. The dataset simulates realistic and noisy network conditions to reflect operational uncertainties and measurement imperfections. SliceVision-F2I is suitable for tasks involving visual learning, network state classification, anomaly detection, and benchmarking of image-based machine learning techniques applied to network data. The dataset is publicly available and can be reused in various research contexts, including multivariate time series analysis, synthetic data generation, and feature-to-image transformations.
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