  We present Knowledge Rocks, an implementation strategy and guideline for
augmenting visualization systems to knowledge-assisted 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 application-agnostic 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.
