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Hans-Jörg Schulz

A Review and Characterization of Progressive Visual Analytics

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A Review and Characterization of Progressive Visual Analytics. / Angelini, Marco; Santucci, Giuseppe; Schumann, Heidrun; Schulz, Hans-Jörg.

In: Informatics, Vol. 5, No. 3, 31, 03.07.2018.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

Angelini, M, Santucci, G, Schumann, H & Schulz, H-J 2018, 'A Review and Characterization of Progressive Visual Analytics', Informatics, vol. 5, no. 3, 31. https://doi.org/10.3390/informatics5030031

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Author

Angelini, Marco ; Santucci, Giuseppe ; Schumann, Heidrun ; Schulz, Hans-Jörg. / A Review and Characterization of Progressive Visual Analytics. In: Informatics. 2018 ; Vol. 5, No. 3.

Bibtex

@article{81d7d0b7527d4e4e845a4b308a973e90,
title = "A Review and Characterization of Progressive Visual Analytics",
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.",
author = "Marco Angelini and Giuseppe Santucci and Heidrun Schumann and Hans-J{\"o}rg Schulz",
year = "2018",
month = "7",
day = "3",
doi = "10.3390/informatics5030031",
language = "English",
volume = "5",
journal = "Informatics",
issn = "2227-9709",
publisher = "MDPI AG",
number = "3",

}

RIS

TY - JOUR

T1 - A Review and Characterization of Progressive Visual Analytics

AU - Angelini, Marco

AU - Santucci, Giuseppe

AU - Schumann, Heidrun

AU - Schulz, Hans-Jörg

PY - 2018/7/3

Y1 - 2018/7/3

N2 - 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.

AB - 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.

U2 - 10.3390/informatics5030031

DO - 10.3390/informatics5030031

M3 - Journal article

VL - 5

JO - Informatics

JF - Informatics

SN - 2227-9709

IS - 3

M1 - 31

ER -