Business model ontologies capture the complex interdependencies between business objects. The analysis of the hence formalized knowledge eludes traditional OLAP systems which operate on numeric measures. Many real-world facts, however, do not boil down to a single number but are more accurately represented by business model ontologies. We adopt business model ontologies for the representation of nonnumeric measures in OLAP cubes. We propose modeling guidelines and adapt traditional OLAP

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... ns for ontologyvalued measures. Ontology-valued measures formalize complex real-world business events that are inadequately represented by numeric measures alone, for example, competitor analyses, production processes. Different business model ontologies can be used for the representation of ontology-valued measures, for example, Resource-Event-Agent (REA) or e 3 value, encoded as RDF data. Dimensions set the context for the knowledge represented by ontologies, yielding cubes of contextualized RDF data. The shared facts at more abstract levels of granularity describe common knowledge shared across different cells of the cube in order to facilitate the analysis with OLAP.