一种结合流式函数数据分析的硬件系统监控可视化分析方法
A Visual Analytics Approach for Hardware System Monitoring with Streaming Functional Data Analysis
AI总结：
针对硬件流式时序数据监控中FDA计算成本高、难适配流式更新的问题，提出结合增量渐进算法生成MS图、搭配FDA主成分分析的可视化分析方法，经真实数据和行业专家验证有效。
摘要：
许多现实世界应用需要分析随时间变化的现象，这类现象本质上具有函数性，由在连续域（如时间）上变化的曲线构成。在分析连续数据时，函数数据分析（functional data analysis, FDA）具备显著优势，例如能够研究导数并约束数据的排序。然而，连续数据固有地具有无限维度，对于长时序数据，FDA方法往往面临计算成本过高的问题。当需要针对持续到达的数据更新FDA结果时，分析问题会变得更具挑战性。本文提出一种可视化分析方法，用于监控和复盘硬件系统产生的流式时序数据，核心是利用FDA识别异常值。为在执行FDA的同时解决计算问题，我们提出了新型增量式与渐进式算法，可快速生成幅值-形状（magnitude-shape, MS）图，该图能够同时呈现时序数据的函数幅值异常性与形状异常性。此外，通过将MS图与FDA版本的主成分分析结合使用，我们提升了分析人员探究视觉识别出的异常值的能力。我们通过两个基于真实世界数据集的使用场景说明了该方法的有效性，最终的工具由行业专家使用真实流式数据集进行了评估。
英文摘要：
Many real-world applications involve analyzing time-dependent phenomena, which are intrinsically functional, consisting of curves varying over a continuum (e.g., time). When analyzing continuous data, functional data analysis (FDA) provides substantial benefits, such as the ability to study the derivatives and to restrict the ordering of data. However, continuous data inherently has infinite dimensions, and for a long time series, FDA methods often suffer from high computational costs. The analysis problem becomes even more challenging when updating the FDA results for continuously arriving data. In this paper, we present a visual analytics approach for monitoring and reviewing time series data streamed from a hardware system with a focus on identifying outliers by using FDA. To perform FDA while addressing the computational problem, we introduce new incremental and progressive algorithms that promptly generate the magnitude-shape (MS) plot, which conveys both the functional magnitude and shape outlyingness of time series data. In addition, by using an MS plot in conjunction with an FDA version of principal component analysis, we enhance the analyst's ability to investigate the visually-identified outliers. We illustrate the effectiveness of our approach with two use scenarios using real-world datasets. The resulting tool is evaluated by industry experts using real-world streaming datasets.
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。
