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VisCARS: Knowledge Graph-Based Context-Aware Recommender System for Time-Series Data Visualization and Monitoring

Pieter Moens, Bruno Volckaert, Sofie Van Hoecke

IEEE Transactions on Visualization and Computer Graphics

|June 13, 2024

Summary

This summary is machine-generated.

This study introduces VisCARS, a context-aware visualization recommender system. It personalizes dashboards for monitoring applications by using knowledge graphs and user preferences, enhancing data visualization efficiency.

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Area of Science:

Data Science
Human-Computer Interaction
Software Engineering

Background:

Creating effective data visualizations requires significant expertise in data models and dashboard applications.
Manual dashboard creation is time-consuming and complex for users.
Monitoring applications necessitate efficient and accurate data presentation.

Purpose of the Study:

To develop a context-aware visualization recommender system (VisCARS) that automates personalized dashboard creation.
To reduce the user's burden of requiring expert knowledge for visualization and dashboard design.
To improve the efficiency and effectiveness of data visualization in monitoring applications.

Main Methods:

Utilized a knowledge graph-based approach to incorporate expert knowledge as contextual features.
Developed a dashboard ontology to semantically annotate the knowledge graph.
Employed knowledge graph embedding, comparison techniques, and context-aware collaborative filtering.

Main Results:

Implemented and integrated VisCARS into a dynamic dashboard solution.
Evaluated the system on a smart healthcare use-case, demonstrating strong performance and scalability.
Showcased superior results compared to state-of-the-art methods.

Conclusions:

VisCARS effectively recommends personalized dashboards by considering system context and user preferences.
The knowledge graph approach enhances the accuracy and relevance of visualization recommendations.
The system shows significant potential for time-critical monitoring applications, improving user experience and data interpretation.