Web-based visual analytics for extreme scale climate science

Steed, Chad A.; Evans, Katherine J.; Harney, John; Jewell, Brian; Shipman, Galen; Smith, Brian E.; Thornton, P. E.; Williams, D. N.

doi:10.1109/bigdata.2014.7004255

Cited by 11 publications

(6 citation statements)

References 23 publications

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“…The concept of VA and VA-related applications have also gained importance in the era of big data in order to make use of large data collections and extreme-scale data [1], e.g. weather and climate data [34]. On the other hand, VA has had influence on computational methods in terms of improving their performance and scalability for real-time visualization of big data [4].…”

Section: Visual Analyticsmentioning

confidence: 99%

Visualization and Interaction for Knowledge Discovery in Simulation Data

Feldkamp¹,

Bergmann²,

Straßburger³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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Discrete-event simulation is an established and popular technology for investigating the dynamic behavior of complex manufacturing and logistics systems. Besides traditional simulation studies that focus on single model aspects, data farming describes an approach for using the simulation model as a data generator for broad scale experimentation with a broader coverage of the system behavior. On top of that we developed a process called knowledge discovery in simulation data that enhances the data farming concept by using data mining methods for the data analysis. In order to uncover patterns and causal relationships in the model, a visually guided analysis then enables an exploratory data analysis. While our previous work mainly focused on the application of suitable data mining methods, we address suitable visualization and interaction methods in this paper. We present those in a conceptual framework followed by an exemplary demonstration in an academic case study.

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Section: Visual Analyticsmentioning

confidence: 99%

Visualization and Interaction for Knowledge Discovery in Simulation Data

Feldkamp¹,

Bergmann²,

Straßburger³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…For comparing the significance of the incoming data stream, analysts are often interested in retrieving the history. Such event detection may also help predict future events, as in the case of simulation modelling [SEH*15], where relationships between model parameters and outputs can be understood based on the definition of events. ET involves the following tasks: ET1 :Understand the importance of current patterns based on the past context. ET2 :Compare current events to the past ones on demand. …”

Section: Problem Characterizationmentioning

confidence: 99%

Human Factors in Streaming Data Analysis: Challenges and Opportunities for Information Visualization

Dasgupta

Arendt

Franklin

et al. 2017

Computer Graphics Forum

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Real‐world systems change continuously. In domains such as traffic monitoring or cyber security, such changes occur within short time scales. This results in a streaming data problem and leads to unique challenges for the human in the loop, as analysts have to ingest and make sense of dynamic patterns in real time. While visualizations are being increasingly used by analysts to derive insights from streaming data, we lack a thorough characterization of the human‐centred design problems and a critical analysis of the state‐of‐the‐art solutions that exist for addressing these problems. In this paper, our goal is to fill this gap by studying how the state of the art in streaming data visualization handles the challenges and reflect on the gaps and opportunities. To this end, we have three contributions in this paper: (i) problem characterization for identifying domain‐specific goals and challenges for handling streaming data, (ii) a survey and analysis of the state of the art in streaming data visualization research with a focus on how visualization design meets challenges specific to change perception and (iii) reflections on the design trade‐offs, and an outline of potential research directions for addressing the gaps in the state of the art.

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“…Social media visual analytics systems are a specific form of text visualization systems (e.g., ThemeRiver [8], EventRiver [9], and TextFlow [10]), which also tend to include a prominent timebased visualization component. But time-based analysis permeates nearly all domains, namely, climate [11,12], cyber security [13], and parallel computing performance monitoring [14]. Falcon can be classified as a domain specific system with general applicability to any quantitative time series data.…”

Section: Related Workmentioning

confidence: 99%

Falcon: Visual analysis of large, irregularly sampled, and multivariate time series data in additive manufacturing

Steed

Halsey

Dehoff

et al. 2017

Computers & Graphics

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Flexible visual analysis of long, high-resolution, and irregularly sampled time series data from multiple sensor streams is a challenge in several domains. In the field of additive manufacturing, this capability is critical for realizing the full potential of large-scale 3D printers. In this paper, we propose a visual analytics approach that helps additive manufacturing researchers acquire a deep understanding of patterns in log and imagery data collected by 3D printers. Specific goals include discovering patterns related to defects and system performance issues, optimizing build configurations to avoid defects, and increasing production efficiency. We introduce Falcon, a new visual analytics system that allows users to interactively explore large, time-oriented data sets from multiple linked perspectives. Falcon provides overviews, detailed views, and unique segmented time series visualizations, all with adjustable scale options. To illustrate the effectiveness of Falcon at providing thorough and efficient knowledge discovery, we present a practical case study involving experts in additive manufacturing and data from a large-scale 3D printer. Although the focus of this paper is on additive manufacturing, the techniques described are applicable to the analysis of any quantitative time series.

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Web-based visual analytics for extreme scale climate science

Cited by 11 publications

References 23 publications

Visualization and Interaction for Knowledge Discovery in Simulation Data

Visualization and Interaction for Knowledge Discovery in Simulation Data

Human Factors in Streaming Data Analysis: Challenges and Opportunities for Information Visualization

Falcon: Visual analysis of large, irregularly sampled, and multivariate time series data in additive manufacturing

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