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Informatics 2018, 5(3), 31; https://doi.org/10.3390/informatics5030031

A Review and Characterization of Progressive Visual Analytics

1
Sapienza University of Rome, 00185 Rome, Italy
2
University of Rostock, 18059 Rostock, Germany
3
Aarhus University, 8000 Aarhus Aarhus, Denmark
Current address: Åbogade 34, 8200 Aarhus N, Denmark.
*
Author to whom correspondence should be addressed.
Received: 16 May 2018 / Revised: 27 June 2018 / Accepted: 30 June 2018 / Published: 3 July 2018
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Abstract

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. View Full-Text
Keywords: visual analytics; progressive visualization; incremental visualization; online algorithms visual analytics; progressive visualization; incremental visualization; online algorithms
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Angelini, M.; Santucci, G.; Schumann, H.; Schulz, H.-J. A Review and Characterization of Progressive Visual Analytics. Informatics 2018, 5, 31.

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