在DeepVATS框架中探索大规模时间序列的可扩展性
Exploring Scalability in Large-Scale Time Series in DeepVATS framework
AI总结：
本文介绍了融合深度学习与可视化分析的DeepVATS工具，通过日志分析考察其在处理不同长度时间序列时的执行与渲染时间演变，验证了该框架的可扩展性。
摘要：
可视化分析对于研究大型时间序列至关重要，因为它能够揭示趋势、异常和洞察。DeepVATS是一个将深度学习与可视化分析相结合的工具，用于分析大型时间序列数据（TS）。它包含三个相互连接的模块。用R语言开发的深度学习模块负责管理数据集和深度学习模型从存储模块的加载与保存。该模块还支持模型训练，并从训练好的模型的潜在空间中获取嵌入向量。存储模块使用Weights and Biases系统运行。随后，这些嵌入向量可以在可视化分析模块中进行分析。该模块基于R Shiny应用程序构建，允许调整与嵌入空间的投影和聚类相关的参数。设置好这些参数后，将显示同时表示嵌入向量和时间序列的交互式图表。本文介绍了该工具，并通过日志分析考察了其可扩展性。在改变时间序列长度的同时，考察了执行时间的演变。这是通过将一个大型数据序列重采样为较小的子集，并记录主要执行和渲染时间以供后续进行可扩展性分析来实现的。
英文摘要：
Visual analytics is essential for studying large time series due to its ability to reveal trends, anomalies, and insights. DeepVATS is a tool that merges Deep Learning (Deep) with Visual Analytics (VA) for the analysis of large time series data (TS). It has three interconnected modules. The Deep Learning module, developed in R, manages the load of datasets and Deep Learning models from and to the Storage module. This module also supports models training and the acquisition of the embeddings from the latent space of the trained model. The Storage module operates using the Weights and Biases system. Subsequently, these embeddings can be analyzed in the Visual Analytics module. This module, based on an R Shiny application, allows the adjustment of the parameters related to the projection and clustering of the embeddings space. Once these parameters are set, interactive plots representing both the embeddings, and the time series are shown. This paper introduces the tool and examines its scalability through log analytics. The execution time evolution is examined while the length of the time series is varied. This is achieved by resampling a large data series into smaller subsets and logging the main execution and rendering times for later analysis of scalability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
