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Evaluating the Memorability and Readability of Micro-filter Visualisations

Topics: Coordinated and Multiple Views; Display and Interaction Technology; Information and Scientific Visualization; Interactive Visual Interfaces for Visualization; Interface and Interaction Techniques for Visualization; Interpretation and Evaluation Methods; Visual Data Analysis and Knowledge Discovery; Visual Representation and Interaction

Authors: Gerwald Tschinkel 1 and Vedran Sabol 2

Affiliations: 1 Know-Center GmbH, Austria ; 2 Know-Center GmbH and Graz University of Technology, Austria

ISBN: 978-989-758-228-8

Keyword(s): Reommendation Visualisation, Filtering, Multiple Views, Visual Information Seeking, Evaluation, Cultural Heritage.

Related Ontology Subjects/Areas/Topics: Abstract Data Visualization ; Computer Vision, Visualization and Computer Graphics ; Coordinated and Multiple Views ; Display and Interaction Technology ; General Data Visualization ; Information and Scientific Visualization ; Interactive Visual Interfaces for Visualization ; Interface and Interaction Techniques for Visualization ; Interpretation and Evaluation Methods ; Visual Data Analysis and Knowledge Discovery ; Visual Representation and Interaction

Abstract: When using classical search engines, researchers are often confronted with a number of results far beyond what they can realistically manage to read; when this happens, recommender systems can help, by pointing users to the most valuable sources of information. In the course of a long-term research project, research into one area can extend over several days, weeks, or even months. Interruptions are unavoidable, and, when multiple team members have to discuss the status of a project, it’s important to be able to communicate the current research status easily and accurately. Multiple type-specific interactive views can help users identify the results most relevant to their focus of interest. Our recommendation dashboard uses micro-filter visualizations intended to improve the experience of working with multiple active filters, allowing researchers to maintain an overview of their progress. Within this paper, we carry out an evaluation of whether micro-visualizations help to increase th e memorability and readability of active filters in comparison to textual filters. Five tasks, quantitative and qualitative questions, and the separate view on the different visualisation types enabled us to gain insights on how micro-visualisations behave and will be discussed throughout the paper. (More)

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Paper citation in several formats:
Tschinkel G. and Sabol V. (2017). Evaluating the Memorability and Readability of Micro-filter Visualisations.In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017) ISBN 978-989-758-228-8, pages 186-197. DOI: 10.5220/0006272001860197

@conference{ivapp17,
author={Gerwald Tschinkel and Vedran Sabol},
title={Evaluating the Memorability and Readability of Micro-filter Visualisations},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017)},
year={2017},
pages={186-197},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006272001860197},
isbn={978-989-758-228-8},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017)
TI - Evaluating the Memorability and Readability of Micro-filter Visualisations
SN - 978-989-758-228-8
AU - Tschinkel G.
AU - Sabol V.
PY - 2017
SP - 186
EP - 197
DO - 10.5220/0006272001860197

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