One of the primary challenges facing scientists is extracting understanding from the large amounts of data produced by simulations, experiments, and observational facilities. The use of data across the entire lifetime ranging from real-time to post-hoc analysis is complex and varied, typically requiring a collaborative effort across multiple teams of scientists. Over time, three sets of tools have emerged: One set for analysis, another for visualization, and a final set for orchestrating the tasks. This trifurcated tool set often results in the manual assembly of analysis and visualization workflows, which are one-off solutions that are often fragile and difficult to generalize. To address these challenges, we propose a serviced-based paradigm and a set of abstractions to guide its design. These abstractions allow for the creation of services that can access and interpret data, and enable interoperability for intelligent scheduling of workflow systems. This work results from a codesign process over analysis, visualization, and workflow tools to provide the flexibility required for production use. Finally, this paper describes a forward-looking research and development plan that centers on the concept of visualization and analysis technology as reusable services, and also describes several realworld use cases that implement these concepts.

Meeting Name

2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 (2020: Oct. 11-14, Toronto, Canada)


Computer Science


This manuscript has been co-authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). This research was supported by the DOE SciDAC RAPIDS Institute and the Exascale Computing Project (17-SC-20-SC),

Keywords and Phrases

High-Performance Computing; in Situ Analysis; Scientific Visualization; Visualization

International Standard Book Number (ISBN)


International Standard Serial Number (ISSN)

1865-0929; 1865-0937

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





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Creative Commons Licensing

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Publication Date

01 Jan 2021