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    This summary is machine-generated.

    Knowledge Rocks offers a guideline to enhance visualization systems with knowledge assistance, improving user decision-making. This framework uses an ontology and database for data analysis and classification in visualization tools.

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    Area of Science:

    • Computer Science
    • Information Visualization

    Background:

    • Visualization systems are increasingly complex, requiring user support for effective data interpretation.
    • Integrating knowledge bases aids users in making informed decisions and drawing accurate conclusions from visualizations.

    Purpose of the Study:

    • To present Knowledge Rocks, an implementation strategy for augmenting visualization systems with knowledge assistance.
    • To provide a general, application-agnostic architecture for knowledge-assisted visualization systems based on the KAVA model.

    Main Methods:

    • Developed an application-agnostic architecture centered around an ontology for automatic data analysis and classification.
    • Linked the ontology to a database for storing classified data instances.
    • Designed a framework (Knowledge Rocks) for integrating knowledge assistance into existing visualization systems.

    Main Results:

    • The Knowledge Rocks framework enables effective reactivation of visualization software resources through knowledge assistance.
    • Demonstrated the broad applicability of the architecture through diverse integration possibilities.
    • Successfully augmented an IT-security system with knowledge-assistance facilities in a case study.

    Conclusions:

    • The proposed architecture and Knowledge Rocks strategy effectively support the development of knowledge-assisted visualization systems.
    • This approach enhances user capabilities in making constructive choices and drawing correct conclusions from complex data.
    • The framework offers a versatile solution for integrating knowledge assistance across various visualization applications.