Toward a Taxonomy for Adaptive Data Visualization in Analytics Applications

Please always quote using this URN: urn:nbn:de:bvb:20-opus-202074
  • Data analytics as a field is currently at a crucial point in its development, as a commoditization takes place in the context of increasing amounts of data, more user diversity, and automated analysis solutions, the latter potentially eliminating the need for expert analysts. A central hypothesis of the present paper is that data visualizations should be adapted to both the user and the context. This idea was initially addressed in Study 1, which demonstrated substantial interindividual variability among a group of experts when freely choosingData analytics as a field is currently at a crucial point in its development, as a commoditization takes place in the context of increasing amounts of data, more user diversity, and automated analysis solutions, the latter potentially eliminating the need for expert analysts. A central hypothesis of the present paper is that data visualizations should be adapted to both the user and the context. This idea was initially addressed in Study 1, which demonstrated substantial interindividual variability among a group of experts when freely choosing an option to visualize data sets. To lay the theoretical groundwork for a systematic, taxonomic approach, a user model combining user traits, states, strategies, and actions was proposed and further evaluated empirically in Studies 2 and 3. The results implied that for adapting to user traits, statistical expertise is a relevant dimension that should be considered. Additionally, for adapting to user states different user intentions such as monitoring and analysis should be accounted for. These results were used to develop a taxonomy which adapts visualization recommendations to these (and other) factors. A preliminary attempt to validate the taxonomy in Study 4 tested its visualization recommendations with a group of experts. While the corresponding results were somewhat ambiguous overall, some aspects nevertheless supported the claim that a user-adaptive data visualization approach based on the principles outlined in the taxonomy can indeed be useful. While the present approach to user adaptivity is still in its infancy and should be extended (e.g., by testing more participants), the general approach appears to be very promising.show moreshow less

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Metadaten
Author: Tristan Poetzsch, Panagiotis Germanakos, Lynn Huestegge
URN:urn:nbn:de:bvb:20-opus-202074
Document Type:Journal article
Faculties:Fakultät für Humanwissenschaften (Philos., Psycho., Erziehungs- u. Gesell.-Wissensch.) / Institut für Psychologie
Language:English
Parent Title (English):Frontiers in Artificial Intelligence
ISSN:2624-8212
Year of Completion:2020
Volume:3
Article Number:9
Source:Frontiers in Artificial Intelligence (2020) 3:9. doi: 10.3389/frai.2020.00009
DOI:https://doi.org/10.3389/frai.2020.00009
Dewey Decimal Classification:1 Philosophie und Psychologie / 15 Psychologie / 150 Psychologie
Tag:analytics; data visualization; graph adaptivity; graph ergonomics; recommendation engine; user model
Release Date:2021/03/02
Date of first Publication:2020/03/20
Open-Access-Publikationsfonds / Förderzeitraum 2020
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International