Dynamic visualization of transient data streams

Wong, Pak Chung; Foote, H.P.; Adams, D.; Cowley, Wendy; Thomas, Jim

doi:10.1109/infvis.2003.1249014

Cited by 24 publications

(6 citation statements)

References 3 publications

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“…Wong et al present a solution to handle large data streams with MDS [105]. They look at the rate of incoming data to decide based on how the chunking process is performed.…”

Section: Custom Chunkingmentioning

confidence: 99%

“…A focus on real-time processing is almost non-existent. Only three approaches that deal with data streams have requirements and recommendations on computation speed [36,43,105]. All other publications deal with large multidimensional data sources with the goal of reducing the long computation times from several minutes to below one minute.…”

Section: Real-time Processingmentioning

confidence: 99%

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A Survey on Progressive Visualization

Ulmer,

Angelini,

Fekete

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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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.

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“…Wong et al present a solution to handle large data streams with MDS [105]. They look at the rate of incoming data to decide based on how the chunking process is performed.…”

Section: Custom Chunkingmentioning

confidence: 99%

Section: Real-time Processingmentioning

confidence: 99%

A Survey on Progressive Visualization

Ulmer,

Angelini,

Fekete

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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“…Scatterplots visualize data instances as points in 2D or 3D spaces. When plotting higher dimension data, the data is typically projected to a lower dimension using multi-dimensional scaling [11] or principal component analysis (PCA). Bar-plots depict the relative frequency or relative density of different data items.…”

Section: Types Of Chartsmentioning

confidence: 99%

Data visualization in internet of things

Protopsaltis

Sarigiannidis

Margounakis³

et al. 2020

Proceedings of the 15th International Conference on Availability, Reliability and Security

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As the Internet of Things (IoT) grows rapidly, huge amounts of wireless sensor networks emerged monitoring a wide range of infrastructure, in various domains such as healthcare, energy, transportation, smart city, building automation, agriculture, and industry producing continuously streamlines of data. Big Data technologies play a significant role within IoT processes, as visual analytics tools, generating valuable knowledge in real-time in order to support critical decision making. This paper provides a comprehensive survey of visualization methods, tools, and techniques for the IoT. We position data visualization inside the visual analytics process by reviewing the visual analytics pipeline. We provide a study of various chart types available for data visualization and analyze rules for employing each one of them, taking into account the special conditions of the particular use case. We further examine some of the most promising visualization tools. Since each IoT domain is isolated in terms of Big Data approaches, we investigate visualization issues in each domain. Additionally, we review visualization methods oriented to anomaly detection. Finally, we provide an overview of the major challenges in IoT visualizations.

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“…• Indefinite computations: These are computations having no defined end by design, such as visualizations for monitoring transient data streams [29][30][31].…”

Section: Reasons For Using Pvamentioning

confidence: 99%

A Review and Characterization of Progressive Visual Analytics

et al. 2018

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Progressive Visual Analytics (PVA) has gained increasing attention over the past years. It brings the user into the loop during otherwise long-running and non-transparent computations by producing intermediate partial results. These partial results can be shown to the user for early and continuous interaction with the emerging end result even while it is still being computed. Yet as clear-cut as this fundamental idea seems, the existing body of literature puts forth various interpretations and instantiations that have created a research domain of competing terms, various definitions, as well as long lists of practical requirements and design guidelines spread across different scientific communities. This makes it more and more difficult to get a succinct understanding of PVA's principal concepts, let alone an overview of this increasingly diverging field. The review and discussion of PVA presented in this paper address these issues and provide (1) a literature collection on this topic, (2) a conceptual characterization of PVA, as well as (3) a consolidated set of practical recommendations for implementing and using PVA-based visual analytics solutions.Keywords: visual analytics; progressive visualization; incremental visualization; online algorithms MotivationWith data growing in size and complexity, and analysis methods getting more sophisticated and computationally intensive, the idea of Progressive Visual Analytics (PVA) [1,2] becomes increasingly appealing. A PVA approach can either subdivide the data to process each data chunk individually, or it can subdivide the analytic process into computational steps that iteratively refine analytic results [3]. By doing so, PVA yields partial results of increasing completeness or approximative results of increasing correctness, respectively. This is useful in a wide range of visual analytics scenarios:• to realize responsive client-server visualizations using incremental data transmissions Because of its versatility, the progressive approach to data analysis and visualization is alternatively seen as a paradigm for computation, for interaction, for data transmission, or for visual presentation. It is thus not surprising that PVA-related research is distributed over multiple disciplines, motivated by various underlying problems, described in different, sometimes overloaded terms at different levels of detail for different audiences.

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Dynamic visualization of transient data streams

Cited by 24 publications

References 3 publications

A Survey on Progressive Visualization

A Survey on Progressive Visualization

Data visualization in internet of things

A Review and Characterization of Progressive Visual Analytics

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