A Visual Analytics Framework for Reviewing Streaming Performance Data

Kesavan, Suraj P.; Fujiwara, Takanori; Li, Jianping Kelvin; Ross, Caitlin; Mubarak, Misbah; Carothers, Christopher D.; Ross, Robert; Ma, Kwan‐Liu

doi:10.1109/pacificvis48177.2020.9280

Cited by 13 publications

(8 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We found that the temporal dimension is visualized in two general ways: continuous or aggregated. Continuous representations are shown with line charts and show a data point for each time step [41,48,55,73]. If the data is too large, a sampling method is applied [29] (see Section A.1 for more on sampling strategies).…”

Section: A Temporal Visualizationmentioning

confidence: 99%

A Survey on Progressive Visualization

Ulmer,

Angelini,

Fekete

et al. 2024

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

Fig. 1: Overview of our taxonomy with its four major categories: visualization, progressive processing, data domain, and visual update pattern. The visualization categories are based on previous taxonomies [47, 65, 85]. The progressive processing categories are an extension of the progressive visual analytics characterization [5], adding a new "Custom chunking" variant. The data domain categories cover the difference in visualization design if the end of the data or process is known or unknown. The fourth category is the visual update pattern which indicates how the visualizations are updated when new partial results are produced.Abstract-Currently, growing data sources and long-running algorithms impede user attention and interaction with visual analytics applications. Progressive visualization (PV) and visual analytics (PVA) alleviate this problem by allowing immediate feedback and interaction with large datasets and complex computations, avoiding waiting for complete results by using partial results improving with time. Yet, creating a progressive visualization requires more effort than a regular visualization but also opens up new possibilities, such as steering the computations towards more relevant parts of the data, thus saving computational resources. However, there is currently no comprehensive overview of the design space for progressive visualization systems. We surveyed the related work of PV and derived a new taxonomy for progressive visualizations by systematically categorizing all PV publications that included visualizations with progressive features. Progressive visualizations can be categorized by well-known visualization taxonomies, but we also found that progressive visualizations can be distinguished by the way they manage their data processing, data domain, and visual update. Furthermore, we identified key properties such as uncertainty, steering, visual stability, and real-time processing that are significantly different with progressive applications. We also collected evaluation methodologies reported by the publications and conclude with statistical findings, research gaps, and open challenges. A continuously updated visual browser of the survey data is available at visualsurvey.net/pva.

show abstract

Section: A Temporal Visualizationmentioning

confidence: 99%

A Survey on Progressive Visualization

Ulmer,

Angelini,

Fekete

et al. 2024

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

show abstract

“…Several approaches eschew the Gantt chart and plot events or metrics about resources in the same space [12,22,24,26,27]. We also aggregate metric data by resource in our metric views.…”

Section: Related Workmentioning

confidence: 99%

Traveler: Navigating Task Parallel Traces for Performance Analysis

Sakin¹,

Bigelow²,

Tohid³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Afterward, for each variable, they applied MDS or t-SNE to the computed similarities and then juxtaposed the DR results for different variables. Kesavan et al [29] extended the same approach for streaming high-dimensional data. In contrast to our framework, these approaches handle only one variable in each DR result.…”

Section: Related Workmentioning

confidence: 99%

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

Fujiwara,

Sakamoto,

Nonaka

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Data-driven problem solving in many real-world applications involves analysis of time-dependent multivariate data, for which dimensionality reduction (DR) methods are often used to uncover the intrinsic structure and features of the data. However, DR is usually applied to a subset of data that is either single-time-point multivariate or univariate time-series, resulting in the need to manually examine and correlate the DR results out of different data subsets. When the number of dimensions is large either in terms of the number of time points or attributes, this manual task becomes too tedious and infeasible. In this paper, we present MulTiDR, a new DR framework that enables processing of time-dependent multivariate data as a whole to provide a comprehensive overview of the data. With the framework, we employ DR in two steps. When treating the instances, time points, and attributes of the data as a 3D array, the first DR step reduces the three axes of the array to two, and the second DR step visualizes the data in a lower-dimensional space. In addition, by coupling with a contrastive learning method and interactive visualizations, our framework enhances analysts' ability to interpret DR results. We demonstrate the effectiveness of our framework with four case studies using real-world datasets.

show abstract

A Visual Analytics Framework for Reviewing Streaming Performance Data

Cited by 13 publications

References 36 publications

A Survey on Progressive Visualization

A Survey on Progressive Visualization

Traveler: Navigating Task Parallel Traces for Performance Analysis

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

Contact Info

Product

Resources

About