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ManiVault: A Flexible and Extensible Visual Analytics Framework for High-Dimensional Data

journalArticle

DOI:10.1109/TVCG.2023.3326582
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Authors: Vieth Alexander / Kroes Thomas / Thijssen Julian / Van Lew Baldur / Eggermont Jeroen / Basu Soumyadeep / Eisemann Elmar / Vilanova Anna / Höllt Thomas / Lelieveldt Boudewijn

Extracted Abstract:

—Exploration and analysis of high-dimensional data are important tasks in many fields that produce large and complex data, like the financial sector, systems biology, or cultural heritage. Tailor-made visual analytics software is developed for each specific application, limiting their applicability in other fields. However, as diverse as these fields are, their characteristics and requirements for data analysis are conceptually similar. Many applications share abstract tasks and data types and are often constructed with similar building blocks. Developing such applications, even when based mostly on existing building blocks, requires significant engineering efforts. We developedManiVault, a flexible and extensible open-source visual analytics framework for analyzing high-dimensional data. The primary objective ofManiVaultis to facilitate rapid prototyping of visual analytics workflows for visualization software developers and practitioners alike.ManiVaultis built using a plugin-based architecture that offers easy extensibility. While our architecture deliberately keeps plugins self-contained, to guarantee maximum flexibility and re-usability, we have designed and implemented a messaging API for tight integration and linking of modules to support common visual analytics design patterns. We provide several visualization and analytics plugins, andManiVault’s API makes the integration of new plugins easy for developers.ManiVaultfacilitates the distribution of visualization and analysis pipelines and results for practitioners through saving and reproducing complete application states. As such, ManiVault can be used as a communication tool among researchers to discuss workflows and results. A copy of this paper and all supplemental material is available at osf.io/9k6jw, and source code at github.com/ManiVaultStudio. Index Terms—High-dimensional data, Visual analytics, Visualization framework, Progressive analytics, Prototyping system. 1

Level 1: Include/Exclude

  • Papers must discuss situated information visualization* (by Willet et al.) in the application domain of CH.
    *A situated data representation is a data representation whose physical presentation is located close to the data’s physical referent(s).
    *A situated visualization is a situated data representation for which the presentation is purely visual – and is typically displayed on a screen.
  • Representation must include abstract data (e.g., metadata).
  • Papers focused solely on digital reconstruction without information visualization aspects are excluded.
  • Posters and workshop papers are excluded to focus on mature research contributions.
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