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

Fujiwara, Takanori; Shilpika,; Sakamoto, Naohisa; Nonaka, Jorji; Yamamoto, Keiji; Ma, Kwan‐Liu

doi:10.1109/tvcg.2020.3028889

Cited by 34 publications

(29 citation statements)

References 48 publications

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“…The projection works as an overview, in which analysts manipulate data and filter parts of interest. Many works combine visualization and automatic algorithms to form a generic tool [FSN ∗ 20,WCR ∗ 17,SZS ∗ 16,LPK ∗ 15,Gle13]. These methods, however, have done good jobs in searching for subsets where patterns exist, but they cannot find patterns among a group of subsets by considering their feature similarity.…”

Section: Related Workmentioning

confidence: 99%

Exploring Multi‐dimensional Data via Subset Embedding

Xie

Tao

et al. 2021

Computer Graphics Forum

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Multi‐dimensional data exploration is a classic research topic in visualization. Most existing approaches are designed for identifying record patterns in dimensional space or subspace. In this paper, we propose a visual analytics approach to exploring subset patterns. The core of the approach is a subset embedding network (SEN) that represents a group of subsets as uniformly‐formatted embeddings. We implement the SEN as multiple subnets with separate loss functions. The design enables to handle arbitrary subsets and capture the similarity of subsets on single features, thus achieving accurate pattern exploration, which in most cases is searching for subsets having similar values on few features. Moreover, each subnet is a fully‐connected neural network with one hidden layer. The simple structure brings high training efficiency. We integrate the SEN into a visualization system that achieves a 3‐step workflow. Specifically, analysts (1) partition the given dataset into subsets, (2) select portions in a projected latent space created using the SEN, and (3) determine the existence of patterns within selected subsets. Generally, the system combines visualizations, interactions, automatic methods, and quantitative measures to balance the exploration flexibility and operation efficiency, and improve the interpretability and faithfulness of the identified patterns. Case studies and quantitative experiments on multiple open datasets demonstrate the general applicability and effectiveness of our approach.

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Section: Related Workmentioning

confidence: 99%

Exploring Multi‐dimensional Data via Subset Embedding

Xie

Tao

et al. 2021

Computer Graphics Forum

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“…However, as they are mainly designed to drive business intelligence type of tasks, their suitability in supporting scientific inquiry may be debatable [17]. Hence, several research efforts were initiated focusing on the development of VA tools for time series analysis further complemented by model building or forecasting functionalities [18,19]. One notable effort relates to the work of Bögl et al [18].…”

Section: Related Work and Best Visualization Practicesmentioning

confidence: 99%

“…A number of general-purpose tools exist that typically achieve one or a few of these design goals, but are lacking capabilities concerning some of the other aspects (see for example, [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]). The review of these existing tools and approaches, along their available visualization solutions, further guided our resolution to reflect on the usability and functionality of diverse visualization techniques in supporting a variety of data exploration tasks and concepts.…”

mentioning

confidence: 99%

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

et al. 2021

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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.

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“…Hu et al (2010) that project 72-dimensional human body keypoints using LLE, or Fujiwara et al (2018) who project entire dimensions (time series) using MDS and t-SNE for computer performance analysis. The latter method was also extended to use PCA and UMAP as 𝑃 𝐵 (Fujiwara et al, 2020).…”

Section: Global (G)mentioning

confidence: 99%

“…Incremental PCA (Ross et al, 2008) projects points in a streaming fashion and is therefore amenable to project time-dependent data. Fujiwara et al (2020) further increase incremental PCA's stability by using Procrustes analysis to align consecutive projections, a method also proposed independently by Joia et al (2011). Neves et al (2020) propose Xtreaming, an incremental technique that handles streaming high-dimensional data by continuously adapting UPDis (Neves et al, 2018), a projection with out-of-sample capability, thus, good stability.…”

Section: Continuous (C)mentioning

confidence: 99%

Visualization of dynamic multidimensional and hierarchical datasets

Vernier

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42 3.3.1 Visual quality 43 3.3.2 Stability 43 3.4 Data 48 3.4.1 Data features 48 3.4.2 Data classes 49 xiii 3.4.3 Datasets 50 3.5 Experimental results 52 3.5.1 Data classi cation analysis 55 3.5.2 Performance analysis across features 57 3.5.3 Comparison of data classes 59 3.6 Discussion and conclusion 60 4 67 4.1 Introduction 67 4.2 Related work 69 4.2.1 Algorithms 69 4.2.2 Metrics 70 4.3 Greedy Insertion Treemap 71 4.4 Evaluation 74 4.4.1 Metrics 74 4.4.2 Techniques 74 4.4.3 Datasets 75 4.5 Results 75 4.5.1 How does GIT's initialization a ect its quality? 75 4.5.2 How do visual quality and stability vary over time? 77 4.5.3 How do all quality metrics vary over all datasets? 79 4.5.4 How to summarize GIT's quality? 79 4.6 Conclusion 83 6 1.4 G-PCA 0 9 9 This chapter is based on the paper "Quantitative Comparison of Dynamic Treemaps for Software Evolution Visualization" (Vernier et al., 2018c) 13

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

Cited by 34 publications

References 48 publications

Exploring Multi‐dimensional Data via Subset Embedding

Exploring Multi‐dimensional Data via Subset Embedding

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

Visualization of dynamic multidimensional and hierarchical datasets

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