Towards a Structural Framework for Explicit Domain Knowledge in Visual Analytics

Rind, Alexander; Wagner, Markus; Aigner, Wolfgang

doi:10.1109/vahc47919.2019.8945032

Cited by 10 publications

(15 citation statements)

References 42 publications

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“…This active part contains general relationships and reasoning used to classify input data. It corresponds to the "concepts" component in the structure defined by Rind et al [40].…”

Section: Requirementsmentioning

confidence: 99%

“…This architecture implements the structure proposed by Rind et al for domain knowledge in visual analytics [40]. Concepts are stored in the acting ontology and datasets in the database.…”

Section: Architecture Definitionmentioning

confidence: 99%

“…The database (DB in Figure 3) stores the "passive" instances and is tightly coupled to the acting ontology by the classification of instances as classes that are contained in the ontology. The nature of these instances is defined in the specific implementation, but we suggest to focus on domain knowledge as proposed by Rind et al [40]. Whether the visualized data and the knowledge base reside in the same database is a design choice -depending on the stored knowledge, having both in the same database can avoid data amplification.…”

Section: Architecture Definitionmentioning

confidence: 99%

See 2 more Smart Citations

Knowledge Rocks:Adding Knowledge Assistance to Visualization Systems

Lohfink,

Anton,

Leitte

et al. 2021

Preprint

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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.

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“…This active part contains general relationships and reasoning used to classify input data. It corresponds to the "concepts" component in the structure defined by Rind et al [40].…”

Section: Requirementsmentioning

confidence: 99%

“…This architecture implements the structure proposed by Rind et al for domain knowledge in visual analytics [40]. Concepts are stored in the acting ontology and datasets in the database.…”

Section: Architecture Definitionmentioning

confidence: 99%

Section: Architecture Definitionmentioning

confidence: 99%

See 1 more Smart Citation

Knowledge Rocks:Adding Knowledge Assistance to Visualization Systems

Lohfink,

Anton,

Leitte

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In spite of these challenges, people still need to have the ability to rapidly compare and contrast information [31] for which VASes can be particularly useful [32]. While there has been previous studies as to the utility of these systems in healthcare and public health settings, such research has been fairly limited up to this point, and further investigation is warranted [33][34][35].…”

Section: Visual Analytics Systems (Vases)mentioning

confidence: 99%

The Online Vaccine Debate: Study of a Visual Analytics System

Ninkov

Sedig

2020

Informatics

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Online debates, specifically the ones about public health issues (e.g., vaccines, medications, and nutrition), occur frequently and intensely, and are having an impact on our world. Many public health topics are debated online, one of which is the efficacy and morality of vaccines. When people examine such online debates, they encounter numerous and conflicting sources of information. This information forms the basis upon which people take a position on such debates. This has profound implications for public health. It necessitates a need for public health stakeholders to be able to examine online debates quickly and effectively. They should be able to easily perform sense-making tasks on the vast amount of online information, such as sentiments, online presence, focus, or geographic locations. In this paper, we report the results of a user study of a visual analytic system (VAS), and whether and how this VAS can help with such sense-making tasks. Specifically, we report a usability evaluation of VINCENT (VIsual aNalytiCs systEm for investigating the online vacciNe debaTe), a VAS previously described. To help the reader, we briefly discuss VINCENT’s design in this paper as well. VINCENT integrates webometrics, natural language processing, data visualization, and human-data interaction. In the reported study, we gave users tasks requiring them to make sense of the online vaccine debate. Thirty-four participants were asked to perform these tasks by investigating data from 37 vaccine-focused websites. Half the participants were given access to the system, while the other half were not. Selected study participants from both groups were subsequently asked to be interviewed by the study administrator. Examples of questions and issues discussed with interviewees were: how they went about completing specific tasks, what they meant by some of the feedback they provided, and how they would have performed on the tasks if they had been placed in the other group. Overall, we found that VINCENT was a highly valuable resource for users, helping them make sense of the online vaccine debate much more effectively and faster than those without the system (e.g., users were able to compare websites similarities, identify emotional tone of websites, and locate websites with a specific focus). In this paper, we also identify a few issues that should be taken into consideration when developing VASes for online public health debates.

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“…Despite the advantages of EMRs, it is often challenging for medical professionals to keep pace with the large quantity of heterogeneous data stored in EMRs [19]. These databases are usually complex and difficult to analyze and interpret.…”

Section: Introductionmentioning

confidence: 99%

VISEMURE: A Visual Analytics System for Making Sense of Multimorbidity Using Electronic Medical Record Data

Nouri

Lizotte

Sedig

et al. 2021

Data

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Multimorbidity is a growing healthcare problem, especially for aging populations. Traditional single disease-centric approaches are not suitable for multimorbidity, and a holistic framework is required for health research and for enhancing patient care. Patterns of multimorbidity within populations are complex and difficult to communicate with static visualization techniques such as tables and charts. We designed a visual analytics system called VISEMURE that facilitates making sense of data collected from patients with multimorbidity. With VISEMURE, users can interactively create different subsets of electronic medical record data to investigate multimorbidity within different subsets of patients with pre-existing chronic diseases. It also allows the creation of groups of patients based on age, gender, and socioeconomic status for investigation. VISEMURE can use a range of statistical and machine learning techniques and can integrate them seamlessly to compute prevalence and correlation estimates for selected diseases. It presents results using interactive visualizations to help healthcare researchers in making sense of multimorbidity. Using a case study, we demonstrate how VISEMURE can be used to explore the high-dimensional joint distribution of random variables that describes the multimorbidity present in a patient population.

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Towards a Structural Framework for Explicit Domain Knowledge in Visual Analytics

Cited by 10 publications

References 42 publications

Knowledge Rocks:Adding Knowledge Assistance to Visualization Systems

Knowledge Rocks:Adding Knowledge Assistance to Visualization Systems

The Online Vaccine Debate: Study of a Visual Analytics System

VISEMURE: A Visual Analytics System for Making Sense of Multimorbidity Using Electronic Medical Record Data

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