Turning the bucket of text into a pipe

Hetzler, Elizabeth G.; Crow, V.L.; Payne, Debbie; Turner, Alan

doi:10.1109/infvis.2005.1532133

Cited by 8 publications

(2 citation statements)

References 9 publications

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“…However, it is interesting that up to 2018, mainly random sampling is used [23,43]. Exceptions are cases when the sampling was determined by streamed data chunks [36] or by user-defined parameters [110]. Only one approach by Jo et al [44] addressed the sampling challenge.…”

Section: Constraints To Data 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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Section: Constraints To Data 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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The Scalable Reasoning System: Lightweight Visualization for Distributed Analytics

Pike

Bruce

Baddeley

et al. 2009

Information Visualization

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A central challenge in visual analytics is the creation of accessible, widely distributable analysis applications that bring the benefits of visual discovery to as broad a user base as possible. Moreover, to support the role of visualization in the knowledge creation process, it is advantageous to allow users to describe the reasoning strategies they employ while interacting with analytic environments. We introduce an application suite called the scalable reasoning system (SRS), which provides web-based and mobile interfaces for visual analysis. The service-oriented analytic framework that underlies SRS provides a platform for deploying pervasive visual analytic environments across an enterprise. SRS represents a ‘lightweight’ approach to visual analytics whereby thin client analytic applications can be rapidly deployed in a platform-agnostic fashion. Client applications support multiple coordinated views while giving analysts the ability to record evidence, assumptions, hypotheses and other reasoning artifacts. We describe the capabilities of SRS in the context of a real-world deployment at a regional law enforcement organization.

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