A Visual Analytics Approach for Hardware System Monitoring with Streaming Functional Data Analysis

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 deri...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics 2022-06, Vol.28 (6), p.2338-2349
Main Authors: Shilpika, Fujiwara, Takanori, Sakamoto, Naohisa, Nonaka, Jorji, Ma, Kwan-Liu
Format: Article
Language:English
Subjects:
Algorithms
Computational efficiency
Computing costs
Continuity (mathematics)
Cost analysis
Data analysis
Data visualization
Datasets
Functional data analysis
Hardware
magnitude-shape plot
Monitoring
Outliers (statistics)
performance visualization
Principal components analysis
progressive data analysis
Time dependence
Time measurement
Time series
Time series analysis
time-series data
Visual analytics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2022.3165348