A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction

Visual analysis of blow molding machine multivariate time series data

Abstract

The recent development in the data analytics field provides a boost in production for modern industries. Small-sized factories intend to take full advantage of the data collected by sensors used in their machinery. The ultimate goal is to minimize cost and maximize quality, resulting in an increase in profit. In collaboration with domain experts, we implemented a data visualization tool to enable decision-makers in a plastic factory to improve their production process. The tool is an interactive dashboard with multiple coordinated views supporting the exploration from both local and global perspectives. In summary, we investigate three different aspects: methods for preprocessing multivariate time series data, clustering approaches for the already refined data, and visualization techniques that aid domain experts in gaining insights into the different stages of the production process. Here we present our ongoing results grounded in a human-centered development process. We adopt a formative evaluation approach to continuously upgrade our dashboard design that eventually meets partners’ requirements and follows the best practices within the field. We also conducted a case study with a domain expert to validate the potential application of the tool in the real-life context. Finally, we assessed the usability and usefulness of the tool with a two-layer summative evaluation that showed encouraging results.

Graphical Abstract

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 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.

EVis: Visually Analyzing Environmentally Driven Events

Earth scientists are increasingly employing time series data with multiple dimensions and high temporal resolution to study the impacts of climate and environmental changes on Earth's atmosphere, biosphere, hydrosphere, and lithosphere. However, the large number of variables and varying time scales of antecedent conditions contributing to natural phenomena hinder scientists from completing more than the most basic analyses. In this paper, we present EVis (Environmental Visualization), a new visual analytics prototype to help scientists analyze and explore recurring environmental events (e.g. rock fracture, landslides, heat waves, floods) and their relationships with high dimensional time series of continuous numeric environmental variables, such as ambient temperature and precipitation. EVis provides coordinated scatterplots, heatmaps, histograms, and RadViz for foundational analyses. These features allow users to interactively examine relationships between events and one, two, three, or more environmental variables. EVis also provides a novel visual analytics approach to allowing users to discover temporally lagging relationships related to antecedent conditions between events and multiple variables, a critical task in Earth sciences. In particular, this latter approach projects multivariate time series onto trajectories in a 2D space using RadViz, and clusters the trajectories for temporal pattern discovery. Our case studies with rock cracking data and interviews with domain experts from a range of sub-disciplines within Earth sciences illustrate the extensive applicability and usefulness of EVis.

Combining 2D and 3D Visualization with Visual Analytics in the Environmental Domain

The application potential of Visual Analytics (VA), with its supporting interactive 2D and 3D visualization techniques, in the environmental domain is unparalleled. Such advanced systems may enable an in-depth interactive exploration of multifaceted geospatial and temporal changes in very large and complex datasets. This is facilitated by a unique synergy of modules for simulation, analysis, and visualization, offering instantaneous visual feedback of transformative changes in the underlying data. However, even if the resulting knowledge holds great potential for supporting decision-making in the environmental domain, the consideration of such techniques still have to find their way to daily practice. To advance these developments, we demonstrate four case studies that portray different opportunities in data visualization and VA in the context of climate research and natural disaster management. Firstly, we focus on 2D data visualization and explorative analysis for climate change detection and urban microclimate development through a comprehensive time series analysis. Secondly, we focus on the combination of 2D and 3D representations and investigations for flood and storm water management through comprehensive flood and heavy rain simulations. These examples are by no means exhaustive, but serve to demonstrate how a VA framework may apply to practical research.