VisCARS : Knowledge Graph-based Context-Aware Recommender System for Time-Series Data Visualization and Monitoring Dashboards
Data visualization recommendation aims to assist the user in creating visualizations from a given dataset. The process of creating appropriate visualizations requires expert knowledge of the available data model as well as the dashboard application that is used. To relieve the user from requiring th...
| Veröffentlicht in: | IEEE transactions on visualization and computer graphics. - 1996. - PP(2024) vom: 13. Juni |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2024
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on visualization and computer graphics |
| Schlagworte: | Journal Article |
| Zusammenfassung: | Data visualization recommendation aims to assist the user in creating visualizations from a given dataset. The process of creating appropriate visualizations requires expert knowledge of the available data model as well as the dashboard application that is used. To relieve the user from requiring this knowledge and from the manual process of creating numerous visualizations or dashboards, we present a context-aware visualization recommender system (VisCARS) for monitoring applications that automatically recommends a personalized dashboard to the user, based on the system they are monitoring and the task they are trying to achieve. Through a knowledge graph-based approach, expert knowledge about the data and the application is included as contextual features to improve the recommendation process. A dashboard ontology is presented that describes key components in a dashboard ecosystem in order to semantically annotate all the knowledge in the graph. The recommender system leverages knowledge graph embedding and comparison techniques in combination with a context-aware collaborative filtering approach to derive recommendations based on the context, i.e., the state of the monitored system, and the end-user preferences. The proposed methodology is implemented and integrated in a dynamic dashboard solution. The resulting recommender system is evaluated on a smart healthcare use-case through a quantitative performance and scalability analysis as well as a qualitative user study. The results highlight the performance of the proposed solution compared to the state-of-the-art and its potential for time-critical monitoring applications |
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| Beschreibung: | Date Revised 25.06.2024 published: Print-Electronic Citation Status Publisher |
| ISSN: | 1941-0506 |
| DOI: | 10.1109/TVCG.2024.3414191 |