The Role of Explicit Knowledge: A Conceptual Model of Knowledge-Assisted Visual Analytics

Lammarsch²,

Computer Graphics Forum

et al. 2018

To complement the currently existing definitions and conceptual frameworks of visual analytics, which focus mainly on activities performed by analysts and types of techniques they use, we attempt to define the expected results of these activities. We argue that the main goal of doing visual analytics is to build a mental and/or formal model of a certain piece of reality reflected in data. The purpose of the model may be to understand, to forecast or to control this piece of reality. Based on this model‐building perspective, we propose a detailed conceptual framework in which the visual analytics process is considered as a goal‐oriented workflow producing a model as a result. We demonstrate how this framework can be used for performing an analytical survey of the visual analytics research field and identifying the directions and areas where further research is needed.

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Viewing Visual Analytics as Model Building

Lammarsch²,

Computer Graphics Forum

et al. 2018

2019 IEEE Workshop on Visual Analytics in Healthcare (VAHC)

“…The nine desiderata are: D1: Explicit knowledge, by definition, resides in the machine space and is machine interpretable [15,47]. Structured forms of knowledge representation allow the VA environment to reason about relationships within the knowledge (e.g., controlled vocabularies with same/different, taxonomies with hierarchical, or ontologies with custom relationships).…”

Section: Desideratamentioning

confidence: 99%

“…effectively using the VA environment, is out of its scope. While the latter can be tackled by usable user interfaces, user onboarding, and automated visualization recommendation, the former is essential for the success analysis, either as explicit knowledge or as tacit knowledge of the domain experts as user [15]. D3: It should be possible to reuse pre-existing taxonomies or other knowledge artifacts that have been established within a community of practice.…”

Section: Desideratamentioning

confidence: 99%

See 1 more Smart Citation

Towards a Structural Framework for Explicit Domain Knowledge in Visual Analytics

Rind¹,

Wagner²,

Aigner³

2019

Self Cite

Clinicians and other analysts working with healthcare data are in need for better support to cope with large and complex data. While an increasing number of visual analytics environments integrates explicit domain knowledge as a means to deliver a precise representation of the available data, theoretical work so far has focused on the role of knowledge in the visual analytics process. There has been little discussion about how such explicit domain knowledge can be structured in a generalized framework. This paper collects desiderata for such a structural framework, proposes how to address these desiderata based on the model of linked data, and demonstrates the applicability in a visual analytics environment for physiotherapy.

Human–Computer Interaction Series

Conclusion

Aigner,

Miksch,

Schumann

et al. 2023

This chapter briefly summarizes the content of the book and describes practical concerns of visualizing time-oriented data in real-world data settings. Visual analytics is briefly outlined as a modern approach that combines visualization, interaction, and computational analysis more tightly to facilitate data analysis activities better. Finally, research opportunities for future work are discussed.