Knowledge-Assisted Visualization and Guidance

Miksch, Silvia; Leitte, Heike; Chen, Min

doi:10.1007/978-3-030-34444-3_4

Cited by 6 publications

(10 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Knowledge Graphs have been proposed to capture users' visual analytics workflows [9]. Using ontology-expressed knowledge to provide user guidance during visual analytics also has some prior art; however, as [10] points out, the guidance generation process is currently poorly understood and expressible in general terms.…”

Section: Knowledge-based Characterization Of Visualizationmentioning

confidence: 99%

Knowledge-driven Exploratory Performance Data Analysis for Execute-Order-Validate Blockchains

Al-Gburi¹,

Kocsis²

2023

Proceedings of the 30th Minisymposium

View full text Add to dashboard Cite

Exploratory data analysis (EDA) of the performance characteristics of complex IT systems, such as enterprise blockchain solutions, would significantly benefit from explicit representations of, and inference on knowledge about the analyzed system. However, connecting EDA and knowledge representation is not part of the current practice. As a novel approach, this paper presents a generic hierarchical activity ontology, connected to Hyperledger Fabric experiments with end-to-end delay, endorsement delay, ordering delay, and block validation observations. On this basis, we present rules for inferring knowledge-based visualization declarations on this ontology. Lastly, we generate Jupyter notebooks for the inferred sequence of visualizations.

show abstract

Section: Knowledge-based Characterization Of Visualizationmentioning

confidence: 99%

Knowledge-driven Exploratory Performance Data Analysis for Execute-Order-Validate Blockchains

Al-Gburi¹,

Kocsis²

2023

Proceedings of the 30th Minisymposium

View full text Add to dashboard Cite

show abstract

“…Knowledge externalization [48] aims to convert it to explicit representations, e.g., protocols, that can be reused or shared. Common applications are collaborative sense-making [50] or knowledge-assisted guidance [31]. Articulation is achieved through direct creation of narratives and diagrams, like causal flow charts [49], or indirect inference from user interaction with tools [12].…”

Section: Motivation and Backgroundmentioning

confidence: 99%

Supporting Domain Characterization in Visualization Design Studies With the Critical Decision Method

Cibulski¹,

Dimara²,

Hermawati³

et al. 2022

2022 IEEE 4th Workshop on Visualization Guidelines in Research, Design, and Education (VisGuides)

View full text Add to dashboard Cite

While domain characterization has become an integral part of visualization design studies, methodological prescriptions are rare. An underrepresented aspect in existing approaches is domain expertise. Knowledge elicitation methods from cognitive science might help but have not yet received much attention for domain characterization. We propose the Critical Decision Method (CDM) to the visualization domain to provide descriptive steps that open up a knowledge-based perspective on domain characterization. The CDM uses retrospective interviews to reveal expert judgment involved in a challenging situation. We apply it to study three domain problems, reflect on our practical experience, and discuss its relevance to domain characterization in visualization research. We found the CDM's realism and subjective nature to be well suited for eliciting cognitive aspects of high-level task performance. Our insights might guide other researchers in conducting domain characterization with a focus on domain knowledge and cognition. With our work, we hope to contribute to the portfolio of meaningful methods used to inform visualization design and to stimulate discussions regarding prescriptive steps for domain characterization.

show abstract

“…Knowledge-assisted visualization aims to incorporate knowledge into the visualization process to support users [32].…”

Section: Knowledge-assisted Visualizationmentioning

confidence: 99%

“…According to Carpendale et al ontologies will be indispensable in developing infrastructures for knowledge-assisted visualization [8]. This application is discussed for example by Miksch et al [32]; examples are the ontologies defined by Sobra et al to support integration and visualization of data from intelligent transportation systems [43] and the three ontologies used by Gilson et al to determine an appropriate visualization for web data (domain, visual representation and semantic bridging ontology) [21].…”

Section: Knowledge-assisted Visualization Systemsmentioning

confidence: 99%

“…Since then, the idea of knowledge assistance was further augmented and formalized. Chen et al provided a solid theoretical basis [12,13,32] and Federico et al developed a conceptual model of knowledgeassisted visual analytics -the KAVA model [18]. Pike et al declare knowledge-based interfaces as an important research challenge [38], and knowledge-assisted visualization was discussed in an issue of the IEEE Computer Graphics and Applications journal [14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Knowledge Rocks:Adding Knowledge Assistance to Visualization Systems

Lohfink,

Anton,

Leitte

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

We present Knowledge Rocks, an implementation strategy and guideline for augmenting visualization systems to knowledgeassisted visualization systems, as defined by the KAVA model. Visualization systems become more and more sophisticated. Hence, it is increasingly important to support users with an integrated knowledge base in making constructive choices and drawing the right conclusions. We support the effective reactivation of visualization software resources by augmenting them with knowledge-assistance. To provide a general and yet supportive implementation strategy, we propose an implementation process that bases on an applicationagnostic architecture. This architecture is derived from existing knowledge-assisted visualization systems and the KAVA model. Its centerpiece is an ontology that is able to automatically analyze and classify input data, linked to a database to store classified instances. We discuss design decisions and advantages of the KR framework and illustrate its broad area of application in diverse integration possibilities of this architecture into an existing visualization system. In addition, we provide a detailed case study by augmenting an it-security system with knowledge-assistance facilities.

show abstract

Knowledge-Assisted Visualization and Guidance

Cited by 6 publications

References 42 publications

Knowledge-driven Exploratory Performance Data Analysis for Execute-Order-Validate Blockchains

Knowledge-driven Exploratory Performance Data Analysis for Execute-Order-Validate Blockchains

Supporting Domain Characterization in Visualization Design Studies With the Critical Decision Method

Knowledge Rocks:Adding Knowledge Assistance to Visualization Systems

Contact Info

Product

Resources

About